This is a link to the QGIS website where you can download open-source GIS software for viewing, analyzing and manipulating geodata like our downloadable shapefiles.

This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps). The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin Seilaplan for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting. The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019. Additional notes for vector data: Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar. The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are: - Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM) - Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed. Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range. This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

The Los Angeles County Storm Drain System is a geometric network model representing the storm drain infrastructure within Los Angeles County. The long term goal of this network is to seamlessly integrate the countywide drainage infrastructure, regardless of ownership or jurisdiction. Current uses by the Department of Public Works (DPW) include asset inventory, operational maintenance, and compliance with environmental regulations.

GIS DATA DOWNLOADS: (More information is in the table below)

File geodatabase: A limited set of feature classes comprise the majority of this geometric network. These nine feature classes are available in one file geodatabase (.gdb). ArcMap versions compatible with the .gdb are 10.1 and later. Read-only access is provided by the open-source software QGIS. Instructions on opening a .gdb file are available here, and a QGIS plugin can be downloaded here.

Acronyms and Definitions (pdf) are provided to better understand terms used.

ONLINE VIEWING: Use your PC’s browser to search for drains by street address or drain name and download engineering drawings. The Web Viewer link is: https://dpw.lacounty.gov/fcd/stormdrain/

MOBILE GIS: This storm drain system can also be viewed on mobile devices as well as your PC via ArcGIS Online. (As-built plans are not available with this mobile option.)

More About these Downloads All data added or updated by Public Works is contained in nine feature classes, with definitions listed below. The file geodatabase (.gdb) download contains these eleven feature classes without network connectivity. Feature classes include attributes with unabbreviated field names and domains.

ArcMap versions compatible with the .gdb are 10.1 and later.

Feature Class Download Description

CatchBasin In .gdb Catch basins collect urban runoff from gutters

Culvert In .gdb A relatively short conduit that conveys storm water runoff underneath a road or embankment. Typical materials include reinforced concrete pipe (RCP) and corrugated metal pipe (CMP). Typical shapes are circular, rectangular, elliptical, or arched.

ForceMain In .gdb Force mains carry stormwater uphill from pump stations into gravity mains and open channels.

GravityMain In .gdb Underground pipes and channels.

LateralLine In .gdb Laterals connect catch basins to underground gravity mains or open channels.

MaintenanceHole In .gdb The top opening to an underground gravity main used for inspection and maintenance.

NaturalDrainage In .gdb Streams and rivers that flow through natural creek beds

OpenChannel In .gdb Concrete lined stormwater channels.

PumpStation In .gdb Where terrain causes accumulation, lift stations are used to pump stormwater to where it can once again flow towards the ocean

Data Field Descriptions

Most of the feature classes in this storm drain geometric network share the same GIS table schema. Only the most critical attributes are listed here per LACFCD operations.

Attribute Description

ASBDATE The date the design plans were approved “as-built” or accepted as “final records”.

CROSS_SECTIN_SHAPE The cross-sectional shape of the pipe or channel. Examples include round, square, trapezoidal, arch, etc.

DIAMETER_HEIGHT The diameter of a round pipe or the height of an underground box or open channel.

DWGNO Drain Plan Drawing Number per LACFCD Nomenclature

EQNUM Asset No. assigned by the Department of Public Works’ (in Maximo Database).

MAINTAINED_BY Identifies, to the best of LAFCD’s knowledge, the agency responsible for maintaining the structure.

MOD_DATE Date the GIS features were last modified.

NAME Name of the individual drainage infrastructure.

OWNER Agency that owns the drainage infrastructure in question.

Q_DESIGN The peak storm water runoff used for the design of the drainage infrastructure.

SOFT_BOTTOM For open channels, indicates whether the channel invert is in its natural state (not lined).

SUBTYPE Most feature classes in this drainage geometric nature contain multiple subtypes.

UPDATED_BY The person who last updated the GIS feature.

WIDTH Width of a channel in feet.

The tile server displays the data and technical specifications aimed at viewing, through any appropriately configured GIS software, the orthophotos published by the Local Information System (SIT) of the Municipality of Bologna.Here you can download the individual tiles (2017)Here you can download the individual tiles (2020)Here you can download the individual tiles (2020)Here you can download the individual tiles (2021)Here you can download the individual tiles (2022)Here you can download the individual tiles (2023)Images are displayed in the WGS 84 Pseudo-Mercator coordinate system (EPSG 3857).The service allows the display of raster images in the form of tiles, previously created and published via a webserver. The technical instructions for viewing can be found in the attached files at the bottom of the page.The images are thus presented very quickly to the user who accesses them. In the manual you will find all the useful references and by way of example, the steps to view the server tile with the QGIS software, a free GIS application.

In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. The plugin ‘Swiss Geo Downloader’, which is available for the open source geoinformation software QGIS, allows freely available Swiss geodata to be downloaded and displayed directly within QGIS. It was developed in 2021 by Patricia Moll in collaboration with the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. In this tutorial we describe how to download the high accuracy elevation model ‘swissALTI3D’ with the help of the ‘Swiss Geo Downloader’ and how to use it for digital planning of a cable line with the plugin ‘Seilaplan’. Please note that the tutorial language is German! Link to the Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader Link to Seilaplan website: https://seilaplan.wsl.ch

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Das Plugin Swiss Geo Downloader, welches für das Open Source Geoinformationssystem QGIS zur Verfügung steht, ermöglicht frei verfügbare Schweizer Geodaten direkt innerhalb von QGIS herunterzuladen und anzuzeigen. Es wurde 2021 von Patricia Moll in Zusammenarbeit mit der eidgenössischen Forschungsanstalt Wald, Schnee und Landschaft WSL entwickelt. In diesem Tutorial beschreiben wir, wie man mit Hilfe des Swiss Geo Downloaders das hochgenaue Höhenmodell swissALTI3D herunterladen und für die Seillinienplanung mit dem Plugin Seilaplan verwenden kann. Link zum Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader Link zur Seilaplan-Webseite: https://seilaplan.wsl.ch

Facilities and features in Chicago parks. For more information, visit http://www.chicagoparkdistrict.com/facilities/search/. To view or use these shapefiles, compression software and special GIS software, such as ESRI ArcGIS or QGIS, is required. To download this file, right-click the "Download" link above and choose "Save link as."

Location of areas of cuttings of deciduous or softwood in the Lot and Garonne department between 2013 and 2014 in order to facilitate the organisation of cutting controls on the department. The controls focus on softwood cuttings, the owners of which are obliged, within 5 years, to restore the stands. The creation of this data is part of a request from the Ministry of Agriculture, Agri-Food and Forestry, to develop a method for detecting clean cuts using free software (qgis) and free satellite data.

This dataset is provided by the WMS service at https://geomap.gsmma.gov.tw/mapguide/mapagent/mapagent.fcgi?version1.0.0&formatimage/png. Please add the URL shown in the downloaded file to GIS software (such as QGIS) and then you can select this layer from the directory.

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains optimum tilt to maximize yearly yield in (°) covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: OPTA LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 2.08 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

The tile server displays the data and technical specifications aimed at viewing, through any appropriately configured GIS software, the orthophotos published by the Local Information System (SIT) of the Municipality of Bologna.Here you can download the individual tiles (2017)Here you can download the individual tiles (2020)Here you can download the individual tiles (2020)Here you can download the individual tiles (2021)Here you can download the individual tiles (2022)Here you can download the individual tiles (2023)Images are displayed in the WGS 84 Pseudo-Mercator coordinate system (EPSG 3857).The service allows the display of raster images in the form of tiles, previously created and published via a webserver. The technical instructions for viewing can be found in the attached files at the bottom of the page.The images are thus presented very quickly to the user who accesses them. In the manual you will find all the useful references and by way of example, the steps to view the server tile with the QGIS software, a free GIS application.

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

Public art located in Chicago parks. To view or use these shapefiles, compression software and special GIS software, such as ESRI ArcGIS or QGIS, is required. To download this file, right-click the "Download" link above and choose "Save link as."

This dataset is provided by WMS service (https://geomap.gsmma.gov.tw/mapguide/mapagent/mapagent.fcgi?version1.0.1&formatimage/png). Please add the URL shown in the downloaded file to GIS software (such as QGIS) to select this layer from the directory.

No parking is allowed on Snow Routes when there is two or more inches on the ground regardless of date or time. To view or use these shapefiles, compression software and special GIS software, such as ESRI ArcGIS or QGIS, is required. To download this file, right-click the "Download" link above and choose "Save link as."

This dataset includes buildings and districts in Chicago which are listed on the National Register of Historic Places (NRHP) or designated as National Historic Landmarks (NHL). The NRHP is the official list of the Nation's historic places worthy of preservation; NHLs are nationally significant historic places designated by the Secretary of the Interior because they possess exceptional value or quality in illustrating or interpreting the heritage of the United States. The NRHP and NHL programs are federally-established and are administered by the National Park Service (www.nps/gov/nr) and the Illinois Historic Preservation Agency (IHPA, www.illinoishistory.gov/). This dataset is provided by the City of Chicago based on NRHP and NHL nominations provided by IHPA. To view or use these shapefiles, compression software and special GIS software, such as ESRI ArcGIS or QGIS, is required. To download this file, right-click the "Download" link above and choose "Save link as." Time Period: Data is current as of June 2012. Update Frequency: Data is updated as needed.

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains solar resource data for: direct normal irradiation (DNI), global horizontal irradiation (GHI), diffuse horizontal irradiation data (DIF), and global irradiation for optimally tilted surfaces (GTI), all in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m). Due to the large amount of data, the coverage has been divided into eight segments. Four segments for the North hemisphere: WWN (West-west-north), WN (West-north), EN (East-north), EEN (East-east-north). Analogically four segments for the South hemisphere: WWS, WS, ES, EES. The data is hyperlinked under 'resources' with the following characteristics: DNI LTAy_AvgDailyTotals (GeoTIFF) Data format: raster (gridded), GEOTIFF File size : 343.99 MB For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

Mosaics are published as ArcGIS image serviceswhich circumvent the need to download or order data. GEO-IDS image services are different from standard web services as they provide access to the raw imagery data. This enhances user experiences by allowing for user driven dynamic area of interest image display enhancement, raw data querying through tools such as the ArcPro information tool, full geospatial analysis, and automation through scripting tools such as ArcPy.Image services are best accessed through the ArcGIS REST APIand REST endpoints (URL's). You can copy the OPS ArcGIS REST API link below into a web browser to gain access to a directory containing all OPS image services. Individual services can be added into ArcPro for display and analysis by using Add Data -> Add Data From Path and copying one of the image service ArcGIS REST endpoint below into the resultant text box. They can also be accessed by setting up an ArcGIS server connectionin ESRI software using the ArcGIS Image Server REST endpoint/URL. Services can also be accessed in open-source software. For example, in QGIS you can right click on the type of service you want to add in the browser pane (e.g., ArcGIS REST Server, WCS, WMS/WMTS) and copy and paste the appropriate URL below into the resultant popup window. All services are in Web Mercator projection.For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Geospatial Ontario (GEO) at geospatial@ontario.caAvailable Products:ArcGIS REST APIhttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/Image Service ArcGIS REST endpoint / URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServerhttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServerWeb Coverage Services (WCS) URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer/WCSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer/WCSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServer/WCSServer/Web Mapping Service (WMS) URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer/WMSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer/WMSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServer/WMSServer/Metadata for all imagery products available in GEO-IDS can be accessed at the links below:South Central Ontario Orthophotography Project (SCOOP) 2023North-Western Ontario Orthophotography Project (NWOOP) 2022Central Ontario Orthophotography Project (COOP) 2021South-Western Ontario Orthophotography Project (SWOOP) 2020Digital Raster Acquisition Project Eastern Ontario (DRAPE) 2019-2020South Central Ontario Orthophotography Project (SCOOP) 2018North-Western Ontario Orthophotography Project (NWOOP) 2017Central Ontario Orthophotography Project (COOP) 2016South-Western Ontario Orthophotography Project (SWOOP) 2015Algonquin Orthophotography Project (2015)Additional Documentation:Ontario Web Raster Services User Guide (Word)Status:Completed: Production of the data has been completed Maintenance and Update Frequency:Annually: Data is updated every yearContact:Geospatial Ontario (GEO), geospatial@ontario.ca

Here is an image of the overall municipal tax rate (foncier bati + habitation, for municipalities and inter-municipalities). http://physaphae.noip.me/Img/2015_Rate_54" alt="Local tax rate 54 of 2015" title="Local tax rate 54 of 2015">

Given that it is at the departmental mesh, it is not useful to include the departmental rate, and national... That would not be part of the comparison.

To do it again yourself you will need: - QQGIS software (Free: https://www.qgis.org/en/site/forusers/download.html), - a qgs file of your department (http://www.actualitix.com/shapefiles-des-departements-de-france.html) - an export of tax rates (https://www.data.gouv.fr/en/datasets/local taxes/)

Procedure: Install QGIS Open your department's .qgs

Add columns - Right click property on the main layer - Go to the fields menu (on the left) - Add (via the pencil) the desired columns (here municipal tax rate, intercommunal built land and housing) - These are reals of a precision 2, and a length 4 - Register

Insert data: - Right click on the layer "Open attribute table" - Select all - Copy - Paste into excel (or openOffice calcs) - Put the ad hoc formulas in excel (SUM.SI.ENS to recover the rate) - Save the desired tab in CSV DOS with the new values - In QGIS > Menu > Layer > Add a delimited text layer - Import the CSV

Present the data: - To simplify I advise you to make one layer per rate, and layers are. Thus rots you in three clicks take out the image of the desired rate - For each layer (or rate) - Right click properties on the csv layer - Labels to add the name of the city and the desired rate - Style for coloring in fct of a csv field

Print the data in pdf: - To print, you need to define a print template - In the menu choose new print dialler - choose the format (a department in A0 is rather readable) - Add vas legend, ladder, and other - Print and voila...

Sidewalks in Chicago. To view or use these shapefiles, compression software, such as 7-Zip, and special GIS software, such as ESRI ArcGIS or QGIS, are required. To download this file, right-click the "Download" link above and choose "Save link as." Once unzipped, the .dbf file may be opened in any spreadsheet program, such as Microsoft Excel, and identifies the address associated with each sidewalk polygon where possible. Note this is a draft file and may be updated periodically.

Buildings located in Chicago Parks. To view or use these shapefiles, compression software and special GIS software, such as ESRI ArcGIS or QGIS, is required. To download this file, right-click the "Download" link above and choose "Save link as."