DNRGPS is an update to the popular DNRGarmin application. DNRGPS and its predecessor were built to transfer data between Garmin handheld GPS receivers and GIS software.

DNRGPS was released as Open Source software with the intention that the GPS user community will become stewards of the application, initiating future modifications and enhancements.

DNRGPS does not require installation. Simply run the application .exe

See the DNRGPS application documentation for more details.

Compatible with: Windows (XP, 7, 8, 10, and 11), ArcGIS shapefiles and file geodatabases, Google Earth, most hand-held Garmin GPSs, and other NMEA output GPSs

Limited Compatibility: Interactions with ArcMap layer files and ArcMap graphics are no longer supported. Instead use shapefile or geodatabase.

Prerequisite: .NET 4 Framework

DNR Data and Software License Agreement

Subscribe to the DNRGPS announcement list to be notified of upgrades or updates.

OpenStreetMap is a project founded in 2004 with the aim of creating a free world map. Volunteers from many countries work on the further development of the software as well as the collection and processing of geodata. Data is collected about roads, railways, rivers, forests, houses and everything else that is commonly seen on maps. The OpenStreetMap data may be used free of charge and processed as long as the source is mentioned (see also: https://www.openstreetmap.org/copyright).

This data set contains an excerpt from the OpenStreetMaps “Planet-File”, which contains the relevant data for the administrative district of Münster. Other formats such as OSM-XML, shapefiles, SVG, Adobe Illustrator, Garmin GPS, GPX, GML, KML, Manifold GIS or raster graphics can be exported at http://wiki.openstreetmap.org/wiki/Export.

For questions about OpenStreetMap data, there is a German-speaking user forum: http://forum.openstreetmap.org/viewforum.php?id=14

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

This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-11-09.

    Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
    These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
    We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.


      This session has 26.16 GB of MP4 files, but no images were trimmed.

GPS information: The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. Device GPS : Emlid Reach M2 Quality of our data - Q1: 86.58 %, Q2: 13.3 %, Q5: 0.12 %

Bathymetry

      The data are collected using a single-beam echosounder S500. 

      We only keep the values which have a GPS correction in Q1.
      We keep the points that are the waypoints.

      We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. 
      The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.
      At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. 
      The size of the grid cells is 1.319 m. 
      The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. 

Generic folder structure

    YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
    ├── DCIM : folder to store videos and photos depending on the media collected. 
    ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
    │  ├── BASE : files coming from rtk station or any static positioning instrument. 
    │  └── DEVICE : files coming from the device. 
    ├── METADATA : folder with general information files about the session. 
    ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
    │  ├── BATHY : output folder for bathymetry raw data extracted from mission logs. 
    │  ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. 
    │  ├── IA : destination folder for image recognition predictions. 
    │  └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. 
    └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.).    

Software All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!

Data DescriptionThe layer on this map contains population, employed labour force counts, private dwelling counts, and employment counts at a Census Subdivision geography from the 2016 Census. The definition of each variable is described next:Population counts: the total population aggregated from different ages in each census tract.Employment counts: the number of labour force aged 15 years and over having an usual work place or working at home at places of work in each census tract, excluding workers with a non-fixed place-of-work.Employed labour force counts: the number of employed labour force aged 15 years and over having a usual work place or working at home at places of residence in each census tract including workers with a non-fixed place-of-work.Private dwellings count: the number of households aggregated from different types of dwellings in each census tract.Note: Population counts are from long census survey forms, covering 25% of the population. The other three variables are from short census survey forms, covering 100% population.Note about the Legend: the Employment and Population values are normalized by Quantiles. Each colour has the same number of features and will not necessarily represent the same values in different layers.InstructionsZoom in and out of the map to update the bar charts. Use the Select Tool to select specific geographies to display on the bar chart.“Select by rectangle” allows you to draw a rectangle and select multiple geography to view in the chart.“Select by point” allows you select an area by clicking on its geography."Add Data" allows you add separate public data as need from ArcGIS Online, URL (an ArcGIS Server Web Service, a WMS OGC Web Service, a KML file, a GeoRSS file, a CSV file), and local files (shapefile, csv, kml, gpx, geojson)

This dataset was collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2023-11-03.

    Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
    These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
    We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

Image acquisition This session has 56.62 GB of MP4 files, which were trimmed into 8778 frames (at 2997/1000 fps).
The frames are georeferenced. 98.74% of these extracted images are useful and 1.26% are useless, according to predictions made by Jacques model. Multilabel predictions have been made on useful frames using DinoVd'eau model.

GPS information: The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. Device GPS : Emlid Reach M2 Quality of our data - Q1: 88.06 %, Q2: 11.68 %, Q5: 0.26 %

Bathymetry

      The data are collected using a single-beam echosounder S500. 

      We only keep the values which have a GPS correction in Q1.
      We keep the points that are the waypoints.

      We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. 
      The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.
      At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. 
      The size of the grid cells is 0.158 m. 
      The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. 

Generic folder structure

    YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
    ├── DCIM : folder to store videos and photos depending on the media collected. 
    ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
    │  ├── BASE : files coming from rtk station or any static positioning instrument. 
    │  └── DEVICE : files coming from the device. 
    ├── METADATA : folder with general information files about the session. 
    ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
    │  ├── BATHY : output folder for bathymetry raw data extracted from mission logs. 
    │  ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. 
    │  ├── IA : destination folder for image recognition predictions. 
    │  └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. 
    └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.).    

Software All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!

Alcance del perímetro ZFE (Zona de Bajas Emisiones) del territorio de la Aglomeración de Annemasse. Datos disponibles en shapefile (EPSG 3946), GPX (EPSG 4326), KML (EPSG 4326). Más información en el sitio web www.annemasse-agglo.fr/zfe.

Datos disponibles en shapefile (EPSG 3946), GPX (EPSG 4326), KML (EPSG 4326).

Más información en el sitio web www.annemasse-agglo.fr/zfe.

This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-12-07.

    Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
    These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
    We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

Image acquisition This session has 28.39 GB of MP4 files, which were trimmed into 9237 frames (at 2997/1000 fps).
The frames are georeferenced. 99.95% of these extracted images are useful and 0.05% are useless, according to predictions made by Jacques model. Multilabel predictions have been made on useful frames using DinoVd'eau model.

GPS information: The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. Device GPS : Emlid Reach M2 Quality of our data - Q1: 78.82 %, Q2: 20.66 %, Q5: 0.52 %

Bathymetry

      The data are collected using a single-beam echosounder ETC 400. 

      We only keep the values which have a GPS correction in Q1.
      We keep the points that are the waypoints.

      We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. 
      The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.
      At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. 
      The size of the grid cells is 0.107 m. 
      The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. 

Generic folder structure

    YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
    ├── DCIM : folder to store videos and photos depending on the media collected. 
    ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
    │  ├── BASE : files coming from rtk station or any static positioning instrument. 
    │  └── DEVICE : files coming from the device. 
    ├── METADATA : folder with general information files about the session. 
    ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
    │  ├── BATHY : output folder for bathymetry raw data extracted from mission logs. 
    │  ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. 
    │  ├── IA : destination folder for image recognition predictions. 
    │  └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. 
    └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.).    

Software All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!

Data DescriptionThe layers on this map contain population, employed labour force counts, private dwelling counts, and employment counts at Census Subdivision and Census Tract geographies from the 2006, 2011, and 2016 Census. The definition of each variable is described next:Population counts: the total population aggregated from different ages in each census tract.Employment counts: the number of labour force aged 15 years and over having an usual work place or working at home at places of work in each census tract, excluding workers with a non-fixed place-of-work.Employed labour force counts: the number of employed labour force aged 15 years and over having a usual work place or working at home at places of residence in each census tract including workers with a non-fixed place-of-work.Private dwellings count: the number of households aggregated from different types of dwellings in each census tract.Note: Population counts are from long census survey forms, covering 25% of the population. The other three variables are from short census survey forms, covering 100% population.Note about the Legend: the Employment and Population values are normalized by Quantiles. Each colour has the same number of features and will not necessarily represent the same values in different layers.InstructionsZoom in and out of the map to update the bar charts. Use the Select Tool to select specific geographies to display on the bar chart.“Select by rectangle” allows you to draw a rectangle and select multiple geography to view in the chart.“Select by point” allows you select an area by clicking on its geography."Add Data" allows you add separate public data as need from ArcGIS Online, URL (an ArcGIS Server Web Service, a WMS OGC Web Service, a KML file, a GeoRSS file, a CSV file), and local files (shapefile, csv, kml, gpx, geojson)Project lead: A.MaruicioDevelopers: C.Riccardo, W.Huang, D.Robbin

OpenStreetMap ist ein im Jahre 2004 gegründetes Projekt mit dem Ziel, eine freie Weltkarte zu erschaffen. Wir sammeln weltweit Daten über Straßen, Eisenbahnen, Flüsse, Wälder, Häuser und alles andere, was gemeinhin auf Karten zu sehen ist. Weil wir die Daten selbst erheben und nicht aus existierenden Karten abmalen, haben wir selbst auch alle Rechte daran. Die OpenStreetMap-Daten darf jeder lizenzkostenfrei einsetzen und beliebig weiterverarbeiten. Dieser Datensatz enthält den Berliner Ausschnitt aus dem Planet File. Weitere Formate wie OSM-XML, shapefiles, SVG,Adobe Illustrator, Garmin GPS, GPX, GML, KML, Manifold GIS, Rastergrafiken können unter http://wiki.openstreetmap.org/wiki/Export exportiert werden.

Fragen zu den OpenStreetMap-Daten können hier diskutiert werden: http://forum.openstreetmap.org/viewforum.php?id=14

This dataset was collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28.

    Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
    These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
    We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

Image acquisition This session has 22.61 GB of MP4 files, which were trimmed into 8035 frames (at 2997/1000 fps).
The frames are georeferenced. 56.91% of these extracted images are useful and 43.09% are useless, according to predictions made by Jacques model. Multilabel predictions have been made on useful frames using DinoVd'eau model.

GPS information: The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. Device GPS : Emlid Reach M2 Quality of our data - Q1: 52.3 %, Q2: 46.01 %, Q5: 1.69 %

Bathymetry

      The data are collected using a single-beam echosounder S500. 

      We only keep the values which have a GPS correction in Q1.
      We keep the points that are the waypoints.

      We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. 
      The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.
      At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. 
      The size of the grid cells is 0.613 m. 
      The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. 

Generic folder structure

    YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
    ├── DCIM : folder to store videos and photos depending on the media collected. 
    ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
    │  ├── BASE : files coming from rtk station or any static positioning instrument. 
    │  └── DEVICE : files coming from the device. 
    ├── METADATA : folder with general information files about the session. 
    ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
    │  ├── BATHY : output folder for bathymetry raw data extracted from mission logs. 
    │  ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. 
    │  ├── IA : destination folder for image recognition predictions. 
    │  └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. 
    └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.).    

Software All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!

OpenStreetMap ist ein im Jahre 2004 gegründetes Projekt mit dem Ziel, eine freie Weltkarte zu erschaffen. Freiwillige aus vielen Ländern arbeiten sowohl an der Weiterentwicklung der Software als auch an der Sammlung und Bearbeitung von Geodaten. Zusammengetragen werden Daten über Straßen, Eisenbahnen, Flüsse, Wälder, Häuser und alles andere, was gemeinhin auf Karten zu sehen ist. Die OpenStreetMap-Daten darf jeder lizenzkostenfrei einsetzen und beliebig weiterverarbeiten, solange die Quelle genannt wird (siehe auch: https://www.openstreetmap.org/copyright).

Dieser Datensatz enthält einen Ausschnitt aus dem OpenStreetMaps "Planet-File", der die relevanten Daten für den Regierungsbezirk Münster enthält. Weitere Formate wie OSM-XML, shapefiles, SVG,Adobe Illustrator, Garmin GPS, GPX, GML, KML, Manifold GIS oder Rastergrafiken können unter https://wiki.openstreetmap.org/wiki/Export exportiert werden.

Für Fragen zu den OpenStreetMap-Daten gibt es ein deutschsprachiges Benutzerforum: https://forum.openstreetmap.org/viewforum.php?id=14

Die Daten werden bereitgestellt von www.geofabrik.de

森林簿等の森林情報やArcGIS Proを利用した画像解析結果に加え、現地調査アプリ（ArcGIS Field Maps）で取得した山地災害情報のリアルタイムデータを重ね合わせることが可能なクラウド型森林GISです。北海道のオープンデータを活用しています。また、Shp、CSV、KML、GPX、GeoJSONファイルをドラッグアンドドロップで直接取り込めます。

This dataset was collected by an Autonomous Surface Vehicle in Etang-Sale, Réunion - 2023-12-13.

    Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
    These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
    We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

Image acquisition This session has 22.32 GB of MP4 files, which were trimmed into 7552 frames (at 2997/1000 fps).
The frames are georeferenced. 99.39% of these extracted images are useful and 0.61% are useless, according to predictions made by Jacques model. Multilabel predictions have been made on useful frames using DinoVd'eau model.

GPS information: The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. Device GPS : Emlid Reach M2 Quality of our data - Q1: 85.05 %, Q2: 14.77 %, Q5: 0.18 %

Bathymetry

      The data are collected using a single-beam echosounder ETC 400. 

      We only keep the values which have a GPS correction in Q1.
      We keep the points that are the waypoints.

      We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. 
      The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.
      At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. 
      The size of the grid cells is 0.076 m. 
      The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. 

Generic folder structure

    YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
    ├── DCIM : folder to store videos and photos depending on the media collected. 
    ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
    │  ├── BASE : files coming from rtk station or any static positioning instrument. 
    │  └── DEVICE : files coming from the device. 
    ├── METADATA : folder with general information files about the session. 
    ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
    │  ├── BATHY : output folder for bathymetry raw data extracted from mission logs. 
    │  ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. 
    │  ├── IA : destination folder for image recognition predictions. 
    │  └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. 
    └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.).    

Software All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!

Data DescriptionThe layer on this map contains population, employed labour force counts, private dwelling counts, and employment counts at a Census Tract geography from the 2006 Census. The definition of each variable is described next:Population counts: the total population aggregated from different ages in each census tract.Employment counts: the number of labour force aged 15 years and over having an usual work place or working at home at places of work in each census tract, excluding workers with a non-fixed place-of-work.Employed labour force counts: the number of employed labour force aged 15 years and over having a usual work place or working at home at places of residence in each census tract including workers with a non-fixed place-of-work.Private dwellings count: the number of households aggregated from different types of dwellings in each census tract.Note: Population counts are from long census survey forms, covering 25% of the population. The other three variables are from short census survey forms, covering 100% population.Note about the Legend: the Employment and Population values are normalized by Quantiles. Each colour has the same number of features and will not necessarily represent the same values in different layers.InstructionsZoom in and out of the map to update the bar charts. Use the Select Tool to select specific geographies to display on the bar chart.“Select by rectangle” allows you to draw a rectangle and select multiple geography to view in the chart.“Select by point” allows you select an area by clicking on its geography."Add Data" allows you add separate public data as need from ArcGIS Online, URL (an ArcGIS Server Web Service, a WMS OGC Web Service, a KML file, a GeoRSS file, a CSV file), and local files (shapefile, csv, kml, gpx, geojson)

森林簿・森林計画図等の森林情報とリアルタイムデータ※を組み合わせた次世代のクラウド型森林GISです。静岡県のオープンデータを活用しています。また、Shp、CSV、KML、GPX、GeoJSONファイルをドラッグアンドドロップで直接取り込めます。※現地調査アプリ（ArcGIS Field Maps）で取得した施業記録やリアルタイム降水量データ（本デモアプリでは未搭載のため、閲覧を希望する場合はお問い合わせください。）

Les couches de cette carte comprennent la population, la population active occupée, les logements privés et le nombre d’emplois dans les secteurs et subdivisions de recensement de 2006, 2011 et 2016. La définition de chaque variable est décrite ci-après :Chiffres de la population : population totale agrégée par tranche d’âge dans chacun des secteurs de recensement.Chiffres de l’emploi : population active occupée âgée de 15 ans et plus ayant un lieu habituel de travail ou travaillant à domicile dans chacun des secteurs de recensement, excluant les travailleurs dont le lieu de travail est variable.Chiffres de la population active occupée : population active occupée âgée de 15 ans et plus ayant un lieu habituel de travail ou travaillant au lieu de résidence, dans chacun des secteurs de recensement, incluant les travailleurs dont le lieu de travail est variable.Chiffres des logements privés : nombre de ménages, agrégés par différents types de logements dans chaque secteur de recensement.Nota : Les chiffres pour la population active occupée proviennent des questionnaires détaillés du recensement, couvrant 25 % de la population. Les trois autres variables proviennent des questionnaires de recensement abrégés, couvrant la totalité de la population.Nota (à propos de la légende) : Les valeurs sur l’emploi et la population sont normalisées par quantile. Chaque couleur présente la même portion des cas, mais ne représente pas nécessairement les mêmes valeurs pour chaque couche.InstructionsFaites un zoom avant et un zoom arrière sur la carte pour mettre à jour les graphiques à barres. Utilisez l’outil de sélection pour sélectionner certaines zones géographiques à afficher dans le graphique à barres.« Sélectionner par rectangle » vous permet de tracer un rectangle et de sélectionner plusieurs zones géographiques à afficher dans le graphique à barres.« Sélectionner par point » vous permet de sélectionner une zone géographique en cliquant dessus.« Ajouter des données » vous permet d’ajouter au besoin des données publiques distinctes provenant d’ArcGIS Online, provenant d’autres sites Web (service Web ArcGIS Server, OpenGISMD Web Map Service, fichier KML, fichier GeoRSS, fichier CSV) et de fichiers locaux (shapefile, csv, kml, gpx, geojson)Conditions d’utilisationLes renseignements utilisés pour construire cette application ont été achetés par le ministère des Transports et ses partenaires municipaux, dans le cadre du consortium de géocodage du lieu de travail du recensement de 2016. Ces renseignements sont fournis par le Ministère à titre de service public et pour plus de convenance et le Ministère ou ses agents n’assument aucune responsabilité, quelle qu’elle soit, et ne garantissent aucunement l’exactitude, le caractère suffisant ou l’exhaustivité de tout renseignement contenu ici ou offert par ce service, notamment, sans toutefois s’y limiter, toute garantie implicite d’adéquation à un usage, une fonctionnalité ou une valeur commerciale en particulier. Les utilisateurs doivent s’assurer de l’exactitude, du caractère suffisant ou de l’exhaustivité des renseignements avant d’agir en fonction de ces renseignements, et ils doivent procéder à toute autre analyse nécessaire à leur utilisation.Le Ministère ne garantit aucunement avoir fourni tous les renseignements pouvant être disponibles.Ce service peut être interrompu en tout temps sans préavis aux utilisateurs.Le Ministère n’assume aucune responsabilité et ne saurait être tenu responsable envers personne de pertes, coûts, dommages ou préjudices, directs ou indirects, occasionnés en tout ou en partie par l’utilisation adéquate ou inadéquate de ce service et des renseignements contenus ici ou de l’absence de renseignements ou envers quiconque sur la foi de mesures ou décisions que vous auriez prises à l’égard de ces renseignements. L’utilisateur assume tous les risques de l’utilisation et de l’application des renseignements.Tout le contenu de cette couche est protégé par la licence ouverte de Statistique Canada et par la licence du gouvernement ouvert – Ontario. Project lead: A.MaruicioDevelopers: C.Riccardo, W.Huang, D.Robbin