OpenStreetMap is a project launched in 2004 to create a free world map. We collect data on roads, railways, rivers, forests, homes and anything else around the world, commonly seen on maps. Because we collect the data yourself and not distinguish from existing cards, we have all the rights to it. Open StreetMap data may be used free of charge by anyone and further processed at any time. This dataset contains the Berlin section of the Planet File. Other formats such as OSM-XML, shapefiles, SVG, Adobe Illustrator, Garmin GPS, GPX, GML, KML, Manifold GIS, grid graphics can be exported at http://wiki.openstreetmap.org/wiki/Export.

Open StreetMap-data questions can be discussed here: Http://forum.openstreetmap.org/viewforum.php?id=14

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

highway IS NOT NULL

Features may have these attributes:

• highway
• surface
• name
• smoothness
• lanes
• width
• source
• layer
• oneway
• bridge

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This data package contains extracts from open datasets to support
the tutorials available at https://github.com/nismod/snail/

This version of the data goes with v0.1 of the tutorials:

https://github.com/nismod/snail/releases/tag/v0.1

WRI Aqueduct Flood Hazard Maps

`flood_layer` contains data extracted and derived from the Aqueduct
Flood Hazard Maps (version 2, updated October 20, 2020).

See https://www.wri.org/resources/data-sets/aqueduct-floods-hazard-maps

These data are shared under the CC-BY Creative Commons Attribution
License 4.0 - https://creativecommons.org/licenses/by/4.0/

Citation: Ward, P.J., H.C. Winsemius, S. Kuzma,
M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et
al. 2020. “Aqueduct Floods Methodology.” Technical Note.
Washington, D.C.: World Resources Institute. Available online at:
www.wri.org/publication/aqueduct-floods-methodology.

Ghana - Subnational Administrative Boundaries

`gha_admbnda_gss_20210308_shp` contains data from Ghana Statistical
Services (GSS) contributed to Humanitarian Data Exchange by the OCHA
Regional Office for West and Central Africa, updated 11 March 2021.

See https://data.humdata.org/m/dataset/ghana-administrative-boundaries

These data are shared under the Creative Commons Attribution for
Intergovernmental Organisations (CC BY-IGO) - https://creativecommons.org/licenses/by/3.0/igo/

Ghana OpenStreetMap Extract

`ghana-latest-free.shp` contains data extracted from OpenStreetMap
and downloaded from GeoFabrik.

The files in this archive have been created from OpenStreetMap data
and are licensed under the Open Database 1.0 License. See
www.openstreetmap.org for details about the project.

This file contains OpenStreetMap data as of 2021-03-22T21:21:57Z.

More recent updates will be made available daily here:

http://download.geofabrik.de/africa/ghana-latest-free.shp.zip

A documentation of the layers in this shape file is available here:

http://download.geofabrik.de/osm-data-in-gis-formats-free.pdf

Ghana Road Network

`GHA_OSM_roads.gpkg` contains data derived from the OpenStreetMap
extract above, and can be reproduced by running through nismod/snail
tutorial 01.

These data are shared under the same Open Database 1.0 License. See
www.openstreetmap.org for details about the project.

Natural Earth Country Boundaries

`ne_10m_admin_0_countries` contains Natural Earth 1:10m Cultural Vectors,
Admin ) - Countries version 4.1.0

See https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/

These data are declared to be in the public domain, and may be shared
and modified without restriction - https://www.naturalearthdata.com/about/terms-of-use/

QGIS project

`overview.qgz` is a QGIS project intended to help preview and explore
the data in this package.

It is shared under the CC-BY Creative Commons Attribution
License 4.0 - https://creativecommons.org/licenses/by/4.0/

Please cite it as part of this data package, by Tom Russell (2021).

Results

`results` contains the results of analysis that can be reproduced
by running through all the nismod/snail tutorials.

These are derived from all the data above, shared under the
combined terms of Open Database 1.0 License and CC-BY licenses as
applicable to derived, extracted and modified data.

Note: updates to this beta layer are currently paused while we sync new versions of the OSM layers for Europe.This feature layer provides access to OpenStreetMap (OSM) highways data for Europe, which is updated every 5 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM line (way) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes highway features defined as a query against the hosted feature layer (i.e. highway is not blank).In OSM, a highway describes any kind of motorway, road, street or path. These features are identified with a highway tag. There are hundreds of different tag values for highway used in the OSM database. In this feature layer, unique symbols are used for several of the most popular highway types, while lesser used types are grouped in an "other" category.Zoom in to large scales (e.g. Streets level or 1:20k scale) to see the highway features display. You can click on a feature to get the name of the highway (if available). The name of the highway will display by default at large scales (e.g. Street level of 1:5k scale). Labels can be turned off in your map if you prefer.Create New LayerIf you would like to create a more focused version of this highway layer displaying just one or two highway types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. highway is path), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. cycleway and pedestrian) that are ready to use, but not for every type of highway.Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

waterway IS NOT NULL OR water IS NOT NULL OR natural IN ('water','wetland','bay')

Features may have these attributes:

• name
• covered
• source
• width
• natural
• depth
• layer
• waterway
• water
• blockage
• tunnel

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

The data extracts of Europe region downloaded on 04/07/2020 was used to create this dataset. From this the highways tagged as motorway, trunk, primary, secondary, tertiary, unclassified and residential are selected and the information was saved as line strings. CRS: WGS84 (EPSG:4326)
Files available are in parquet and csv format. Please feel free to convert the files in to desired file formats.

This dataset is an extraction of streets and pathways from OpenStreetMap data made by WFP that follow UNSDIT standards. The data is updated in near-real time from OSM servers and include all latest updates. NOTE: this dataset doesn't include main roads that have been published on a separate dataset (main roads).

More documentation on the whole process for extracting OpenStreetMap roads can be found here: http://geonode.wfp.org/documents/6823/download

This dataset is an extraction of streets and pathways from OpenStreetMap data made by WFP that follow UNSDIT standards. The data is updated in near-real time from OSM servers and include all latest updates. NOTE: this dataset doesn't include main roads that have been published on a separate dataset (main roads).

More documentation on the whole process for extracting OpenStreetMap roads can be found here: http://geonode.wfp.org/documents/6823/download

CANUE staff developed the Green Roads data set by combining street network files from Open Street Map 9OSM) (downloaded Nov 29, 2020) and annual average normalized difference vegetation index (NDVI) data from LandSat 8 circa 2016 from Google Earth Engine. OSM roads categorized as primary, secondary, tertiary, tertiary link, residential, unclassified and unknown were extracted from OSM, combined into a single file and clipped to urban areas. Urban areas were defined as all dissemination blocks classified as small population centres (population 1,000 to 29,999), medium population centres (population 30,000 to 99,999) or large population centres (population 100,000 or greater) in the 2016 Census. The urban roads layer was used to extract all LandSat 8 pixels with NDVI data (30m resolution). All extracted pixels with an NDVI value of 0.3 or greater, indicating green vegetation, were converted into points. Finally, the total number or points and the average NDVI value was calculated within buffers of 250m, 500m, 750m and 1000m of DMTI single-link postal codes from 2016.

CANUE staff developed the Green Roads data set by combining street network files from Open Street Map 9OSM) (downloaded Nov 29, 2020) and annual average normalized difference vegetation index (NDVI) data from LandSat 8 circa 2016 from Google Earth Engine. OSM roads categorized as primary, secondary, tertiary, tertiary link, residential, unclassified and unknown were extracted from OSM, combined into a single file and clipped to urban areas. Urban areas were defined as all dissemination blocks classified as small population centres (population 1,000 to 29,999), medium population centres (population 30,000 to 99,999) or large population centres (population 100,000 or greater) in the 2016 Census. The urban roads layer was used to extract all LandSat 8 pixels with NDVI data (30m resolution). All extracted pixels with an NDVI value of 0.3 or greater, indicating green vegetation, were converted into points. Finally, the total number or points and the average NDVI value was calculated within buffers of 250m, 500m, 750m and 1000m of DMTI single-link postal codes from 2016.

Statistical open data on LAU regions of Slovakia, Czech Republic, Poland, Hungary (and other countries in the future). LAU1 regions are called counties, okres, okresy, powiat, járás, járási, NUTS4, LAU, Local Administrative Units, ... and there are 733 of them in this V4 dataset. Overall, we cover 733 regions which are described by 137.828 observations (panel data rows) and more than 1.760.229 data points.

This LAU dataset contains panel data on population, on age structure of inhabitants, on number and on structure of registered unemployed. Dataset prepared by Michal Páleník. Output files are in json, shapefiles, xls, ods, json, topojson or CSV formats. Downloadable at zenodo.org.

This dataset consists of:

data on unemployment (by gender, education and duration of unemployment),

data on vacancies,

open data on population in Visegrad counties (by age and gender),

data on unemployment share.

Combined latest dataset

dataset of the latest available data on unemployment, vacancies and population

dataset includes map contours (shp, topojson or geojson format), relation id in OpenStreetMap, wikidata entry code,

it also includes NUTS4 code, LAU1 code used by national statistical office and abbreviation of the region (usually license plate),

source of map contours is OpenStreetMap, licensed under ODbL

no time series, only most recent data on population and unemployment combined in one output file

columns: period, lau, name, registered_unemployed, registered_unemployed_females, disponible_unemployed, low_educated, long_term, unemployment_inflow, unemployment_outflow, below_25, over_55, vacancies, pop_period, TOTAL, Y15-64, Y15-64-females, local_lau, osm_id, abbr, wikidata, population_density, area_square_km, way

Slovakia – SK: 79 LAU1 regions, data for 2024-10-01, 1.659 data,

Czech Republic – CZ: 77 LAU1 regions, data for 2024-10-01, 1.617 data,

Poland – PL: 380 LAU1 regions, data for 2024-09-01, 6.840 data,

Hungary – HU: 197 LAU1 regions, data for 2024-10-01, 2.955 data,

13.071 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 79 77 380 197

lau LAU code of the region 79 77 380 197

name name of the region in local language 79 77 380 197

registered_unemployed number of unemployed registered at labour offices 79 77 380 197

registered_unemployed_females number of unemployed women 79 77 380 197

disponible_unemployed unemployed able to accept job offer 79 77 0 0

low_educated unmployed without secondary school (ISCED 0 and 1) 79 77 380 197

long_term unemployed for longer than 1 year 79 77 380 0

unemployment_inflow inflow into unemployment 79 77 0 0

unemployment_outflow outflow from unemployment 79 77 0 0

below_25 number of unemployed below 25 years of age 79 77 380 197

over_55 unemployed older than 55 years 79 77 380 197

vacancies number of vacancies reported by labour offices 79 77 380 0

pop_period date of population data 79 77 380 197

TOTAL total population 79 77 380 197

Y15-64 number of people between 15 and 64 years of age, population in economically active age 79 77 380 197

Y15-64-females number of women between 15 and 64 years of age 79 77 380 197

local_lau region's code used by local labour offices 79 77 380 197

osm_id relation id in OpenStreetMap database 79 77 380 197

abbr abbreviation used for this region 79 77 380 0

wikidata wikidata identification code 79 77 380 197

population_density population density 79 77 380 197

area_square_km area of the region in square kilometres 79 77 380 197

way geometry, polygon of given region 79 77 380 197

Unemployment dataset

time series of unemployment data in Visegrad regions

by gender, duration of unemployment, education level, age groups, vacancies,

columns: period, lau, name, registered_unemployed, registered_unemployed_females, disponible_unemployed, low_educated, long_term, unemployment_inflow, unemployment_outflow, below_25, over_55, vacancies

Slovakia – SK: 79 LAU1 regions, data for 334 periods (1997-01-01 ... 2024-10-01), 202.082 data,

Czech Republic – CZ: 77 LAU1 regions, data for 244 periods (2004-07-01 ... 2024-10-01), 147.528 data,

Poland – PL: 380 LAU1 regions, data for 189 periods (2005-03-01 ... 2024-09-01), 314.100 data,

Hungary – HU: 197 LAU1 regions, data for 106 periods (2016-01-01 ... 2024-10-01), 104.408 data,

768.118 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 26 386 18 788 71 772 20 882

lau LAU code of the region 26 386 18 788 71 772 20 882

name name of the region in local language 26 386 18 788 71 772 20 882

registered_unemployed number of unemployed registered at labour offices 26 386 18 788 71 772 20 882

registered_unemployed_females number of unemployed women 26 386 18 788 62 676 20 882

disponible_unemployed unemployed able to accept job offer 25 438 18 788 0 0

low_educated unmployed without secondary school (ISCED 0 and 1) 11 771 9855 41 388 20 881

long_term unemployed for longer than 1 year 24 253 9855 41 388 0

unemployment_inflow inflow into unemployment 26 149 16 478 0 0

unemployment_outflow outflow from unemployment 26 149 16 478 0 0

below_25 number of unemployed below 25 years of age 11 929 9855 17 100 20 881

over_55 unemployed older than 55 years 11 929 9855 17 100 20 882

vacancies number of vacancies reported by labour offices 11 692 18 788 62 676 0

Population dataset

time series on population by gender and 5 year age groups in V4 counties

columns: period, lau, name, gender, TOTAL, Y00-04, Y05-09, Y10-14, Y15-19, Y20-24, Y25-29, Y30-34, Y35-39, Y40-44, Y45-49, Y50-54, Y55-59, Y60-64, Y65-69, Y70-74, Y75-79, Y80-84, Y85-89, Y90-94, Y_GE95, Y15-64

Slovakia – SK: 79 LAU1 regions, data for 28 periods (1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 152.628 data,

Czech Republic – CZ: 78 LAU1 regions, data for 24 periods (2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 125.862 data,

Poland – PL: 382 LAU1 regions, data for 29 periods (1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 626.941 data,

Hungary – HU: 197 LAU1 regions, data for 11 periods (2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023), 86.680 data,

992.111 data in total.

column/number of observations description SK CZ PL HU

period period (month and year) the data is for 6636 5574 32 883 4334

lau LAU code of the region 6636 5574 32 883 4334

name name of the region in local language 6636 5574 32 883 4334

gender gender (male or female) 6636 5574 32 883 4334

TOTAL total population 6636 5574 32 503 4334

Y00-04 inhabitants between 00 to 04 years inclusive 6636 5574 32 503 4334

Y05-09 number of inhabitants between 05 to 09 years of age 6636 5574 32 503 4334

Y10-14 number of people between 10 to 14 years inclusive 6636 5574 32 503 4334

Y15-19 number of inhabitants between 15 to 19 years of age 6636 5574 32 503 4334

Y20-24 number of people between 20 to 24 years inclusive 6636 5574 32 503 4334

Y25-29 number of inhabitants between 25 to 29 years of age 6636 5574 32 503 4334

Y30-34 inhabitants between 30 to 34 years inclusive 6636 5574 32 503 4334

Y35-39 number of inhabitants between 35 to 39 years of age 6636 5574 32 503 4334

Y40-44 inhabitants between 40 to 44 years inclusive 6636 5574 32 503 4334

Y45-49 number of inhabitants younger than 49 and older than 45 years 6636 5574 32 503 4334

Y50-54 inhabitants between 50 to 54 years inclusive 6636 5574 32 503 4334

Y55-59 number of inhabitants between 55 to 59 years of age 6636 5574 32 503 4334

Y60-64 inhabitants between 60 to 64 years inclusive 6636 5574 32 503 4334

Y65-69 number of inhabitants younger than 69 and older than 65 years 6636 5574 32 503 4334

Y70-74 inhabitants between 70 to 74 years inclusive 6636 5574 24 670 4334

Y75-79 number of inhabitants between 75 to 79 years of age 6636 5574 24 670 4334

Y80-84 number of people between 80 to 84 years inclusive 6636 5574 24 670 4334

Y85-89 number of inhabitants younger than 89 and older than 85 years 6636 5574 0 0

Y90-94 inhabitants between 90 to 94 years inclusive 6636 5574 0 0

Y_GE95 number of people 95 years or older 6636 3234 0 0

Y15-64 number of people between 15 and 64 years of age, population in economically active age 6636 5574 32 503 4334

Notes

more examples at www.iz.sk

NUTS4 / LAU1 / LAU codes for HU and PL are created by me, so they can (and will) change in the future; CZ and SK NUTS4 codes are used by local statistical offices, so they should be more stable

NUTS4 codes are consistent with NUTS3 codes used by Eurostat

local_lau variable is an identifier used by local statistical office

abbr is abbreviation of region's name, used for map purposes (usually cars' license plate code; except for Hungary)

wikidata is code used by wikidata

osm_id is region's relation number in the OpenStreetMap database

Example outputs

you can download data in CSV, xml, ods, xlsx, shp, SQL, postgis, topojson, geojson or json format at 📥 doi:10.5281/zenodo.6165135

Counties of Slovakia – unemployment rate in Slovak LAU1 regions

Regions of the Slovak Republic

Unemployment of Czechia and Slovakia – unemployment share in LAU1 regions of Slovakia and Czechia

interactive map on unemployment in Slovakia

Slovakia – SK, Czech Republic – CZ, Hungary – HU, Poland – PL, NUTS3 regions of Slovakia

download at 📥 doi:10.5281/zenodo.6165135

suggested citation: Páleník, M. (2024). LAU1 dataset [Data set]. IZ Bratislava. https://doi.org/10.5281/zenodo.6165135

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

A list of bicycle shops in Glasgow. Data extracted by running Overpass API query where 'shop=bicycle' for Glasgow City council boundaries from Overpass turbo. Contains OpenStreetMap data licensed under Creative Commons Attribution-ShareAlike for datasets published before 12th September 2012 and Open Data Commons Open Database License for data published afterwards. (c) OpenStreetMap contributors Data Mining by Overpass API Licence: None bikeshops080814.geojson - https://dataservices.open.glasgow.gov.uk/Download/Organisation/d6b3a908-e124-46e1-ba53-1c5cb3411623/Dataset/fa919906-99eb-45fa-b886-cf4c19ea1e50/File/8935f0a0-6fed-4603-81e6-66fef0abcd93/Version/3718a7a7-0e0b-4ef1-99c6-fe19ef673e64 cycle-shops.zip - https://dataservices.open.glasgow.gov.uk/Download/Organisation/d6b3a908-e124-46e1-ba53-1c5cb3411623/Dataset/fa919906-99eb-45fa-b886-cf4c19ea1e50/File/590a2c39-ef01-4bde-8e47-f4274a3ee7c2/Version/808c9aea-dbfc-408d-a896-8225ccdde644

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.

Introduction

We are enclosing the database used in our research titled "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary", along with our statistical calculations. For the sake of reproducibility, further information can be found in the file Short_Description_of_Data_Analysis.pdf and Statistical_formulas.pdf

The sharing of data is part of our aim to strengthen the base of our scientific research. As of March 7, 2024, the detailed submission and analysis of our research findings to a scientific journal has not yet been completed.

The dataset was expanded on 23rd September 2024 to include SPSS statistical analysis data, a heatmap, and buffer zone analysis around the Health Development Offices (HDOs) created in QGIS software.

Short Description of Data Analysis and Attached Files (datasets):

Our research utilised data from 2022, serving as the basis for statistical standardisation. The 2022 Hungarian census provided an objective basis for our analysis, with age group data available at the county level from the Hungarian Central Statistical Office (KSH) website. The 2022 demographic data provided an accurate picture compared to the data available from the 2023 microcensus. The used calculation is based on our standardisation of the 2022 data. For xlsx files, we used MS Excel 2019 (version: 1808, build: 10406.20006) with the SOLVER add-in.

Hungarian Central Statistical Office served as the data source for population by age group, county, and regions: https://www.ksh.hu/stadat_files/nep/hu/nep0035.html, (accessed 04 Jan. 2024.) with data recorded in MS Excel in the Data_of_demography.xlsx file.

In 2022, 108 Health Development Offices (HDOs) were operational, and it's noteworthy that no developments have occurred in this area since 2022. The availability of these offices and the demographic data from the Central Statistical Office in Hungary are considered public interest data, freely usable for research purposes without requiring permission.

The contact details for the Health Development Offices were sourced from the following page (Hungarian National Population Centre (NNK)): https://www.nnk.gov.hu/index.php/efi (n=107). The Semmelweis University Health Development Centre was not listed by NNK, hence it was separately recorded as the 108th HDO. More information about the office can be found here: https://semmelweis.hu/egeszsegfejlesztes/en/ (n=1). (accessed 05 Dec. 2023.)

Geocoordinates were determined using Google Maps (N=108): https://www.google.com/maps. (accessed 02 Jan. 2024.) Recording of geocoordinates (latitude and longitude according to WGS 84 standard), address data (postal code, town name, street, and house number), and the name of each HDO was carried out in the: Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file.

The foundational software for geospatial modelling and display (QGIS 3.34), an open-source software, can be downloaded from:

https://qgis.org/en/site/forusers/download.html. (accessed 04 Jan. 2024.)

The HDOs_GeoCoordinates.gpkg QGIS project file contains Hungary's administrative map and the recorded addresses of the HDOs from the

Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file,

imported via .csv file.

The OpenStreetMap tileset is directly accessible from www.openstreetmap.org in QGIS. (accessed 04 Jan. 2024.)

The Hungarian county administrative boundaries were downloaded from the following website: https://data2.openstreetmap.hu/hatarok/index.php?admin=6 (accessed 04 Jan. 2024.)

HDO_Buffers.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding buffer zones with a radius of 7.5 km.

Heatmap.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding heatmap (Kernel Density Estimation).

A brief description of the statistical formulas applied is included in the Statistical_formulas.pdf.

Recording of our base data for statistical concentration and diversification measurement was done using MS Excel 2019 (version: 1808, build: 10406.20006) in .xlsx format.

• Aggregated number of HDOs by county: Number_of_HDOs.xlsx
• Standardised data (Number of HDOs per 100,000 residents): Standardized_data.xlsx
• Calculation of the Lorenz curve: Lorenz_curve.xlsx
• Calculation of the Gini index: Gini_Index.xlsx
• Calculation of the LQ index: LQ_Index.xlsx
• Calculation of the Herfindahl-Hirschman Index: Herfindahl_Hirschman_Index.xlsx
• Calculation of the Entropy index: Entropy_Index.xlsx
• Regression and correlation analysis calculation: Regression_correlation.xlsx

Using the SPSS 29.0.1.0 program, we performed the following statistical calculations with the databases Data_HDOs_population_without_outliers.sav and Data_HDOs_population.sav:

• Regression curve estimation with elderly population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_elderly_without_outlier.spv
• Pearson correlation table between the total population, elderly population, and number of HDOs per county, excluding outlier values such as Budapest and Pest County: Pearson_Correlation_populations_HDOs_number_without_outliers.spv.
• Dot diagram including total population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_total_population_without_outliers.spv.
• Dot diagram including elderly (64<) population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_elderly_population_without_outliers.spv
• Regression curve estimation with total population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_without_outlier.spv
• Dot diagram including elderly (64<) population and number of HDOs per county: Dot_HDO_elderly_population.spv
• Dot diagram including total population and number of HDOs per county: Dot_HDO_total_population.spv
• Pearson correlation table between the total population, elderly population, and number of HDOs per county: Pearson_Correlation_populations_HDOs_number.spv
• Regression curve estimation with total population and number of HDOs, (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_total_population.spv

For easier readability, the files have been provided in both SPV and PDF formats.

The translation of these supplementary files into English was completed on 23rd Sept. 2024.

If you have any further questions regarding the dataset, please contact the corresponding author: domjan.peter@phd.semmelweis.hu

A log of dataset alerts open, monitored or resolved on the open data portal. Alerts can include issues as well as deprecation or discontinuation notices.

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 ...

IncRML resources

This Zenodo dataset contains all the resources of the paper 'IncRML: Incremental Knowledge Graph Construction from Heterogeneous Data Sources' submitted to the Semantic Web Journal's Special Issue on Knowledge Graph Construction. This resource aims to make the paper experiments fully reproducible through our experiment tool written in Python which was already used before in the Knowledge Graph Construction Challenge by the ESWC 2023 Workshop on Knowledge Graph Construction. The exact Java JAR file of the RMLMapper (rmlmapper.jar) is also provided in this dataset which was used to execute the experiments. This JAR file was executed with Java OpenJDK 11.0.20.1 on Ubuntu 22.04.1 LTS (Linux 5.15.0-53-generic). Each experiment was executed 5 times and the median values are reported together with the standard deviation of the measurements.

Datasets

We provide both dataset dumps of the GTFS-Madrid-Benchmark and of real-life use cases from Open Data in Belgium.
GTFS-Madrid-Benchmark dumps are used to analyze the impact on execution time and resources, while the real-life use cases aim to verify the approach on different types of datasets since the GTFS-Madrid-Benchmark is a single type of dataset which does not advertise changes at all.

Benchmarks

• GTFS-Madrid-Benchmark: change types with fixed data size and amount of changes: additions-only, modifications-only, deletions-only (11 versions)
• GTFS-Madrid-Benchmark: amount of changes with fixed data size: 0%, 25%, 50%, 75%, and 100% changes (11 versions)
• GTFS-Madrid-Benchmark: data size with fixed amount of changes: scales 1, 10, 100 (11 versions)

Real-world datasets

• Traffic control center Vlaams Verkeerscentrum (Belgium): traffic board messages data (1 day, 28760 versions)
• Meteorological institute KMI (Belgium): weather sensor data (1 day, 144 versions)
• Public transport agency NMBS (Belgium): train schedule data (1 week, 7 versions)
• Public transport agency De Lijn (Belgium): busses schedule data (1 week, 7 versions)
• Bike-sharing company BlueBike (Belgium): bike-sharing availability data (1 day, 1440 versions)
• Bike-sharing company JCDecaux (EU): bike-sharing availability data (1 day, 1440 versions)
• OpenStreetMap (World): geographical map data (1 day, 1440 versions)

Ingestion

Real-world datasets LDES output was converted into SPARQL UPDATE queries and executed against Virtuoso to have an estimate for non-LDES clients how incremental generation impacted ingestion into triplestores.

Remarks

1. The first version of each dataset is always used as a baseline. All next versions are applied as an update on the existing version. The reported results are only focusing on the updates since these are the actual incremental generation.
2. GTFS-Change-50_percent-{ALL, CHANGE}.tar.xz datasets are not uploaded as GTFS-Madrid-Benchmark scale 100 because both share the same parameters (50% changes, scale 100). Please use GTFS-Scale-100-{ALL, CHANGE}.tar.xz for GTFS-Change-50_percent-{ALL, CHANGE}.tar.xz
3. All datasets are compressed with XZ and provided as a TAR archive, be aware that you need sufficient space to decompress these archives! 2 TB of free space is advised to decompress all benchmarks and use cases. The expected output is provided as a ZIP file in each TAR archive, decompressing these requires even more space (4 TB).

Reproducing

By using our experiment tool, you can easily reproduce the experiments as followed:

1. Download one of the TAR.XZ archives and unpack them.
2. Clone the GitHub repository of our experiment tool and install the Python dependencies with 'pip install -r requirements.txt'.
3. Download the rmlmapper.jar JAR file from this Zenodo dataset and place it inside the experiment tool root folder.
4. Execute the tool by running: './exectool --root=/path/to/the/root/of/the/tarxz/archive --runs=5 run'. The argument '--runs=5' is used to perform the experiment 5 times.
5. Once executed, you can generate the statistics by running: './exectool --root=/path/to/the/root/of/the/tarxz/archive stats'.

Testcases

Testcases to verify the integration of RML and LDES with IncRML, see https://doi.org/10.5281/zenodo.10171394

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['waterway'] IS NOT NULL OR tags['water'] IS NOT NULL OR tags['natural'] IN ('water','wetland','bay')

Features may have these attributes:

• name
• name:en
• waterway
• covered
• width
• depth
• layer
• blockage
• tunnel
• natural
• water
• source
• name:si
• name:ta

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Context

This dataset is a byproduct of my phd thesis. It combines the Archaeological Settlements of Turkey (TAY) Project data with geo spatial data obtained from openstreetmaps.

Content

For each archaeological settlement, the data contains:

• active dates:
• geo spatial data which points to the town containing the settlement.
• information with respect to site type and its research status/methodology.
  These are all contained in the file taydata.json.

The associated notebook to this dataset gives how each file is produced.

We give several important statistics with respect to regions, and cities of Turkey for the Iron Age.

If you want to visualize the data on a map. You can use the 1200_330_bce_sites_of_turkey.umap file.
Just download the file and visualize it on umap or on framacarte

Acknowledgements

Without the immense effort of TAY Project and its researchers, this dataset would not be possible.