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

tags['place'] IN ('isolated_dwelling', 'town', 'village', 'hamlet', 'city')

Features may have these attributes:

• name
• name:en
• place
• population
• is_in
• source
• name:lv

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 dataset was extracted from OpenStreetMap (OSM) across the geographic area of Australia on 02 December 2021. Its purpose is to display Places of Worship as an area (polygon) within Australia. Note, however, as this dataset is built by a community of mappers, there is no guarantee of its spatial or attribute accuracy. Use at your own risk. For more information about the map features represented in this dataset (including their attributes), refer to the OpenStreetMap Wiki. Please note: The original data for this dataset has been downloaded from Geofabrik on 02 December 2021. Due to changes in tagging, previous versions of OSM may not be comparable with this release.

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

place IN ('isolated_dwelling','town','village','hamlet','city')

Features may have these attributes:

• source
• population
• place
• is_in
• name

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 feature layer provides access to OpenStreetMap (OSM) amenity data for Australia and Oceania, which is updated every 5 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM point (node) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes amenity features defined as a query against the hosted feature layer (i.e. amenity is not blank).In OSM, amenities are useful and important facilities for visitors and residents, such as places of worship, restaurants, banks, and schools. These features are identified with an amenity tag. There are thousands of different tag values used in the OSM database. In this feature layer, unique symbols are used for several of the most popular amenity types, while lesser used types are grouped in an "other" category.Zoom in to large scales (e.g. City level or 1:80k scale) to see the amenity features display. You can click on a feature to get the name of the amenity. The name of the amenity will display by default at very large scales (e.g. Building level of 1:2k 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 amenity layer displaying just one or two amenity 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. amenity is fire station), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri will publish a few such layers (e.g. Schools, Medical Facilities, and Places of Worship) that are ready to use, but not for every type of amenity.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.

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

tags['place'] IN ('isolated_dwelling', 'town', 'village', 'hamlet', 'city')

Features may have these attributes:

• name
• name:en
• place
• population
• is_in
• source

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

tags['place'] IN ('isolated_dwelling', 'town', 'village', 'hamlet', 'city')

Features may have these attributes:

• name
• name:en
• place
• population
• is_in
• source

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 dataset contains the latest Open Street Map (OSM) objects for Greece, including the following elements: 1) Buildings, 2) Land Use Information, 3) Natural Objects, 4) Places, 5) Places of Faith and Worship, 6) Points of Interest, 7) Railway Networks, 8) Road Networks, 9) Points of Traffic Interest, 10) Mixed Transportation Hubs, 11) Water Bodies, and 12) Waterways.

Information and data were collected from: www.geofabrik.de

Το εν λόγω σύνολο δεδομένων περιλαμβάνει τα νεότερα Open Street Map (OSM) αντικείμενα για την Ελλάδα. Περιλαμβάνει τα ακόλουθα αντικείμενα: 1) Κτίρια, 2) Χρήσεις Γης, 3) Φυσικά Αντικείμενα, 4) Τοποθεσίες, 5) Χώροι Λατρείας, 6) Σημεία Ενδιαφέροντος, 7) Σιδηροδρομικά Δίκτυα, 8) Οδικά Δίκτυα, 9) Σημεία Συγκοινωνιακού Ενδιαφέροντος, 10) Συγκοινωνιακοί Κόμβοι, 11) Υδάτινοι Φορείς και 12) Υδάτινοι Δίαυλοι.

Οι επιμέρους πληροφορίες και δεδομένα συλλέχθηκαν από το: www.geofabrik.de

Exports du découpage administratif français au niveau communal (contours des communes) issu d'OpenStreetMap produit dans sa grande majorité à partir du cadastre. Ces données sont issues du crowdsourcing effectué par les contributeurs au projet OpenStreetMap et sont sous licence ODbL qui impose un partage à l'identique et la mention obligatoire d'attribution doit être "© les contributeurs d'OpenStreetMap sous licence ODbL" conformément à http://osm.org/copyright Un export automatique quotidien au format shapefile est disponible, ainsi qu'un second export avec des géométries allégées et vérifiées topologiquement (pas de chevauchement). Descriptif du contenu des fichiers "communes" Origine Les données proviennent de la base de données cartographiques OpenStreetMap. Celles-ci ont été constituées à partir du cadastre mis à disposition par la DGFiP sur cadastre.gouv.fr. En complément sur Mayotte où le cadastre n'est pas disponible sur cadastre.gouv.fr, ce sont les limites du GEOFLA de l'IGN qui ont été utilisées ainsi que le tracé des côtes à partir des images aériennes de Bing. Plus d'infos: http://prev.openstreetmap.fr/36680-communes Format Ces fichiers sont proposés au format shapefile, en projection WGS84 avec plusieurs niveaux de détails: simplification à 5m simplification à 50m simplification à 100m La topologie est conservée lors du processus de simplification (cf: http://prev.openstreetmap.fr/blogs/cquest/limites-administratives-simplifiees) Contenu Ces fichiers contiennent l'ensemble des communes françaises, y compris les DOM, Mayotte et Saint-Pierre-et-Miquelon. Pour Paris, Lyon, Marseille, ce sont les limites d'arrondissements qui sont fournies à la place des limites de communes. Pour chaque commune ou arrondissement, les attributs suivants sont ajoutés: insee: code INSEE à 5 caractères de la commune nom: nom de la commune (tel que figurant dans OpenStreetMap, si possible conforme aux règles de toponymie) wikipedia: entrée wikipédia (code langue suivi du nom de l'article) surf_ha : surface en hectares de la commune Pour les communes de Bois-Guillaume et Bihorel, les données du GEOFLA ont été corrigées manuellement suite à l'annulation de la fusion le 1er Janvier 2014. Historique 19-12-2013 : première génération du fichier, basé sur le découpage communal OSM au 19-12-2013 20-12-2013 : correction de 2 erreurs (un cimetière militaire était exclu du territoire par erreur, la géométrie de la commune de Landerneau manquait) 06-03-2014 : troisième génération du fichier, basé sur le découpage communal OSM au 06-03-2014 29-06-2014 : remplacement des limites de Paris, Lyon, Marseille par les limites de leurs arrondissements municipaux. 30-06-2014 : ajout de la version "enrichie" 10-10-2014 : ajout d'un export CSV de la version "enrichie" 01-01-2015 : quatrième version du fichier, prenant en compte les fusions de communes au 1/1/2015 ainsi que les communes ayant changé de nom le 3/12/2014. Pour les communes fusionnées et à titre temporaire (attente de publication du COG 2015) le code INSEE conservé est celui de la commune où le chef-lieu est fixé par le JORF. 19-01-2016 : cinquième version du fichier, prenant en compte les fusions de communes au 19/1/2016 ainsi que les changements intervenus en 2015 (nouveaux noms, etc). Voir ce jeu de données pour plus de détail sur les fusions et communes nouvelles. Les arrondissements municipaux (Paris, Lyon, Marseille) sont disponibles dans un fichier séparé avec les communes déléguées issues de la création des communes nouvelles. 11-01-2017 : version prenant en compte les 181 fusions de communes au 1/1/2017 ainsi que les changements intervenus en 2016 (nouveaux noms, fusions, etc). Voir ce jeu de données pour plus de détail sur les fusions et communes nouvelles. 01-01-2018 : version comprenant les 33 fusions de communes au 1/1/2018 et la fusion au 1/7/2017, ainsi que les changements de noms intervenus en 2017. St-Pierre et Miquelon sont dans le fichier des communes des Collectivités d'Outre-Mer. 26-02-2018 : version au 1/1/2018 complétée par 3 communes nouvelles (Aubessagne, Blancs-Coteaux, Geiswiller-Zœbersdorf) soit au total 36 fusions. 01-01-2019 : version au 1/1/2019 comprenant 232 communes nouvelles répertoriées sur wikipédia au 01/01/2019 11-02-2019 : intégration des communes nouvelles répertoriées par l'INSEE 07-03-2019 : correction du code INSEE de La Léchère (73187) 01-01-2021 : Fusion des communes au 1/1/2021 Versions précédentes disponibles sur: http://osm13.openstreetmap.fr/~cquest/openfla/export/ Pour toute question concernant ces exports, vous pouvez contacter exports@openstreetmap.fr Voir aussi : Liste des adjacences des communes françaises Contours des EPCI 2014 et Contours des EPCI 2013 Contours des arrondissements français Contours des départements français et Cartes SVG des départements Contours de régions françaises Contours des futures régions

Last update: August 20, 2024OverviewThis point data was generated and filtered from OpenStreetMap and is intended to represent places of interest in the state of Utah. These may include businesses, restaurants, places of worship, airports, parks, schools, event centers, apartment complexes, hotels, car dealerships…almost anything that you can find in OpenStreetMap (OSM). There are over 23,000 features in the original dataset (March 2022) and users can directly contribute to it through openstreetmap.org. This data is updated approximately once every month and will likely continue to grow over time with user activity.Data SourcesThe original bulk set of OSM data for the state of Utah is downloaded from Geofabrik: https://download.geofabrik.de/north-america/us/utah-latest-free.shp.zipAdditional attributes for the Utah features are gathered via the Overpass API using the following query: https://overpass-turbo.eu/s/1geRData Creation ProcessThe Open Source Places layer is created by a Python script that pulls statewide OSM data from a nightly archive provided by Geofabrik (https://www.geofabrik.de/data/download.html). The archive data contains nearly 20 shapefiles, some that are relevant to this dataset and some that aren't. The Open Source Places layer is built by filtering the polygon and point data in those shapefiles down to a single point feature class with specific categories and attributes that UGRC determines would be of widest interest. The polygon features (buildings, areas, complexes, etc.) are converted to points using an internal centroid. Spatial filtering is done as the data from multiple shapefiles is combined into a single layer to minimize the occurrence of duplicate features. (For example, a restaurant can be represented in OSM as both a point of interest and as a building polygon. The spatial filtering helps reduce the chances that both of these features are present in the final dataset.) Additional de-duplication is performed by using the 'block_id' field as a spatial index, to ensure that no two features of the same name exist within a census block. Then, additional fields are created and assigned from UGRC's SGID data (county, city, zip, nearby address, etc.) via point-in-polygon and near analyses. A numeric check is done on the 'name' field to remove features where the name is less than 3 characters long or more than 50% numeric characters. This eliminates several features derived from the buildings layer where the 'name' is simply an apartment complex building number (ex: 3A) or house number (ex: 1612). Finally, additional attributes (osm_addr, opening_hours, phone, website, cuisine, etc.) are pulled from the Overpass API (https://wiki.openstreetmap.org/wiki/Overpass_API) and joined to the filtered data using the 'osm_id' field as the join key.Field Descriptionsaddr_dist - the distance (m) to the nearest UGRC address point within 25 mosm_id - the feature ID in the OSM databasecategory - the feature's data class based on the 4-digit code and tags in the OSM databasename - the name of the feature in the OSM databasecounty - the county the feature is located in (assigned from UGRC's county boundaries)city - the city the feature is located in (assigned from UGRC's municipal boundaries)zip - the zip code of the feature (assigned from UGRC's approximation of zip code boundaries)block_id - the census block the feature is located in (assigned from UGRC's census block boundaries)ugrc_addr - the nearest address (within 25 m) from the UGRC address point databasedisclaimer - a note from UGRC about the ugrc_near_addr fieldlon - the approximate longitude of the feature, calculated in WGS84 EPSG:4326lat - the approximate latitude of the feature, calculated in WGS84 EPSG:4326amenity - the amenity available at the feature (if applicable), often similar to the categorycuisine - the type of food available (if applicable), multiple types are separated by semicolons (;)tourism - the type of tourist location, if applicable (zoo, viewpoint, hotel, attraction, etc.)shop - the type of shop, if applicablewebsite - the feature's website in the OSM database, if availablephone - the feature's phone number(s) in the OSM database, if availableopen_hours - the feature's operating hours in the OSM database, if availableosm_addr - the feature's address in the OSM database, if availableMore information can be found on the UGRC data page for this layer:https://gis.utah.gov/data/society/open-source-places/

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

tags['place'] IN ('isolated_dwelling', 'town', 'village', 'hamlet', 'city')

Features may have these attributes:

• name
• name:en
• place
• population
• is_in
• source
• name:he

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.

La thématique Couverture du sol OSM comporte une seule couche (osm_fr_couverture_sol en français, osm_en_landcover en anglais) contenant tous les objets polygonaux du territoire choisi portant l’attribut OSM aeroway=aerodrome, place=island, place=islet, place=square ou les clés OSM landuse, leisure et natural traduits en français par aeroport=aérodrome, lieu=île, lieu=îlot, lieu=place dans une ville, usage_du_sol, loisirs et nature.

Cette couche affiche dans sa table d'attributs les clés les plus couramment utilisées dans OSM sur cette thématique, ainsi qu'un champ jsonb contenant l'ensemble des attributs OSM de l'objet.

Elle est produite par LLg en continu via imposm run à partir des extraits et réplication minute fournis par OSM France (le décalage avec le serveur principal OSM peut être vérifié ici : http://munin.openstreetmap.fr/osm14.openstreetmap.fr/download.vm.openstreetmap.fr/osm_replication_lag_osmbin.html).

Consulter la fiche parente pour disposer de plus d'information, accéder aux données sur les zones disponibles ou aux autres thématiques.

This dataset is a comprehensive inventory of Alaskan buildings, storage tanks, and roads that were: (1) detected from 0.5 meter resolution satellite imagery of communities (acquired between 2018-2023) and (2) supplemented by OpenStreetMap data. We created HABITAT (High-resolution Arctic Built Infrastructure and Terrain Analysis Tool), a deep learning-based, high-performance computing-enabled mapping pipeline to automatically detect buildings and roads from high-resolution Maxar satellite imagery across the Arctic region. Shapefiles beginning with "HABITAT_AK" contain only the post-processed deep learning predictions. Shapefiles beginning with "HABITAT_OSM" contain the post-processed deep learning predictions supplemented by OpenStreetMap data. The HABITAT pipeline is based on a ResNet50-UNet++ semantic segmentation architecture trained on a training dataset comprised of building and road footprint polygons manually digitized from Maxar satellite imagery across the circumpolar Arctic (including Alaska, Russia, and Canada). The code is made available at https://github.com/PermafrostDiscoveryGateway/HABITAT. From imagery of 285 Alaskan communities acquired between 2018-2023, we detected approximately 250,000 buildings and storage tanks (comprising a 41.76 million square meter footprint) and 15 million meters of road. Building (including storage tanks) footprint polygons and road centerlines were strictly mapped within the boundaries of Alaskan communities (both incorporated places and census designated places). After the deep learning model detected building and road footprints, post-processing was performed to smooth out building footprints, extract centerlines from road footprints, and remove falsely-detected infrastructure. In particular, a buffer is created around developed land cover identified by the 2016 Alaska National Land Cover Database map, and model predictions that fall outside of the buffer are assumed to be confused with non-infrastructure land cover. Finally, we selected buildings and roads from the OpenStreetMap Alaska dataset (downloaded in June 2024 from https://download.geofabrik.de/) that do not intersect with any deep learning predictions to generate a merged OSM and HABITAT infrastructure dataset. This merged product comprises a total building footprint of 53 million square meters and a road network of 63,744 km across the state of Alaska.

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

tags['place'] IN ('isolated_dwelling', 'town', 'village', 'hamlet', 'city')

Features may have these attributes:

• name
• name:en
• place
• population
• is_in
• source
• name:ar

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.

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:

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

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.

Contenu Carte de chaque département extrait d'Open Street Map, mise à jour chaque semaine. NB : Les régions sont également disponibles. Chaque département est livré sous forme d'une archive ZIP qui contient plusieurs couches cartographiques au fomat shapefile (le .doc de la cartographie) : places.shp : noms des villes ou des quartiers roads.shp : toutes les voies de passage de l'autoroute au chemin piéton buildings.shp : l'espace bâti raillways.shp : les voies ferrées waterways.shp :le réseau hydrolique points.shp : une liste de point d’intérêt natural.shp : zones vertes landuse.shp : occupation des sol admin-departement.shp : le département Par ailleurs, un fichier projet QGis très sommaire est fourni dans l'archive, afin de visualiser la superposition des couches dans un outil libre. Origine Les données proviennent de la base de données cartographique communautaire et libre OpenStreetMap. Le découpage par département provient du Contours des départements français issus d'OpenStreetMap. Les shapefile sont extraits selon la méthode exposée par l'excellent article de Maxime Résibois sur PortailSIG : http://www.portailsig.org/content/recuperer-des-donnees-openstreetmap-gdalogr Les sources du traitement automatique d'extraction sont disponibles sur github. Elles s'appuyent sur tuttle, un système de build pour les données. Licence Ces données sont issues du crowdsourcing effectué par les contributeurs au projet OpenStreetMap et sont sous licence ODbL qui impose un partage à l'identique et la mention obligatoire d'attribution doit être "© les contributeurs d'OpenStreetMap sous licence ODbL" conformément à http://osm.org/copyright Historique des modifications 7 avril 2018 correction d'un bug qui pouvait retirer une partie de la donnée sur les départements côtiers mise à jour du fichier QGis pour que la carte soit plus jolie de près Départements et covid Si votre département est soumis au pass sanitaire, commandez le vôtre au format carte bancaire sur carte-sanitaire.fr

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.

Reason for Selection This indicator captures the recreational value and opportunities to connect with nature provided by greenways and trails. Greenways and trails provide many well-established social and economic benefits ranging from improving human health, reducing traffic congestion and air and noise pollution, increasing property values, and generating new jobs and business revenue (ITRE 2018). The locations of greenways and trails are regularly updated through the open-source database OpenStreetMap, while data on condition are regularly updated through the National Land Cover Database (NLCD). Input Data

Base Blueprint 2022 extent
OpenStreetMap data “roads” layer, accessed 2-27-2023 

A line from this dataset is considered a potential greenway/trail if the value in the “fclass” attribute is either bridleway, cycleway, footway, or path. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more on the OSM copyright page.

2019 National Land Cover Database (NLCD): Percent developed imperviousness
Southeast Blueprint 2023 extent

Mapping Steps The greenways and trails indicator score reflects both the natural condition and connected length of the greenway/trail. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Natural condition Natural condition is based on the amount of impervious surface surrounding the greenway/trail. Since perceptions of a greenway’s “naturalness” are influenced both by the immediate surroundings adjacent to the path, and the greater viewshed, natural condition is calculated by averaging two measurements: local impervious and nearby impervious.

Local impervious is defined as the percent impervious surface of the 30 m pixel that intersects the trail. Nearby impervious is defined as the average impervious surface within a 300 m radius circle surrounding the path (note: along a 300 m stretch of trail, we only count the impervious surface within a 45 m buffer on either side of the trail, since pixels nearer the trail have a bigger impact on the greenway/trail experience). The natural classes are defined as follows: 3 = Mostly natural: average of local and nearby impervious is ≤1% 2 = Partly natural: average of local and nearby impervious is >1 and <10% 1 = Developed: average of local and nearby impervious is ≥10% Connected length The connected length of the path is calculated using the entire extent of the potential greenways/trails dataset. A trail is considered connected to another trail if it is within 2 m of the other trail. Length thresholds are defined by typical lengths of three common recreational greenway activities: walking, running, and biking. The 40 km threshold for biking is based on the standard triathlon biking segment of 40 km (~25 mi). Because a 5K is the most common road race distance, the running threshold is set at 5 km (~3.1 mi) (Running USA 2017). The 1.9 km (1.2 mi) walking threshold is based on the average walking trip on a summer day (U.S. DOT 2002).

Using the statistics software R, download the OpenStreetMap data for the continental Southeast area. 
Select all lines from the OpenStreetMap data that have a highway tag of either footway, cycleway, bridleway, or path. These are all considered potential trails. 
Removed all lines marked as private.
Identify lines from the potential trails that are tagged as sidewalks. Assign them a value of 1 in the indicator.

Final scores If the potential greenway/trail was tagged as a sidewalk in the “other tags” field, it is given a value of 1 to separate sidewalks from what most people think of as a trail or greenway. If a pixel does not intersect a potential greenway/trail but overlaps with a value that is not NoData in the 2019 NLCD impervious surface layer, it is coded with a value of 0. Then clip to the spatial extent of Base Blueprint 2022. As a final step, clip to the spatial extent of Southeast Blueprint 2023. Final indicator values Indicator values are assigned as follows: 7 = Mostly natural and connected for ≥40 km 6 = Mostly natural and connected for 5 to <40 km or partly natural and connected for≥40 km 5 = Mostly natural and connected for 1.9 to <5 km, partly natural and connected for 5 to <40 km, or developed and connected for ≥40 km 4 = Mostly natural and connected for <1.9 km, partly natural and connected for 1.9 to <5 km, or developed and connected for 5 to <40 km 3 = Partly natural and connected for <1.9 km or developed and connected for 1.9 to <5 km 2 = Developed and connected for <1.9 km 1 = Sidewalk 0 = Not identified as trail, sidewalk, or other path Known Issues

This indicator sometimes misclassifies sidewalks as greenways and trails because they are not tagged as a sidewalk in the OpenStreetMap data. 
This indicator occasionally misclassifies driveways as “sidewalks and other paths” in places where they are not correctly tagged as private in OpenStreetMap. These typically appear as isolated pixels receiving a score of 1 on the indicator. 
OpenStreetMap does not provide a complete inventory of greenways and trails in the Southeast. Paths that are missing from the source data will be underprioritized in this indicator. For example, some trails are missing within National Wildlife Refuges.
This indicator includes trails and sidewalks from OpenStreetMap, which is a crowdsourced dataset. While members of the OpenStreetMap community often verify map features to check for accuracy and completeness, there is the potential for spatial errors (e.g., misrepresenting the path of a greenway) or incorrect tags (e.g., mislabeling a path as a footway that is actually a road for vehicles). However, using a crowdsourced dataset gives on-the-ground experts, Blueprint users, and community members the power to fix errors and add new greenways and trails to improve the accuracy and coverage of this indicator in the future. 
This indicator sometimes underestimates greenway length when connections route under bridges or along abandoned dirt roads. Some of these issues have been fixed through active testing and improvement, but some likely remain.
When calculating nearby impervious for one greenway, if there’s another greenway within 300 m, impervious surface from the different but overlapping greenway buffer area is also used to compute natural condition. This is an unintended issue with the analysis methods. Investigation into potential fixes is ongoing.
The indicator doesn’t currently include areas where future greenways are planned.

Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov). Literature Cited American Planning Association. 2018. Recommendations for Future Enhancements to the Blueprint. [https://secassoutheast.org/pdf/Recommendations-for-Future-Enhancements-to-the-Blueprint-FINAL.pdf].

Institute for Transportation Research and Education (ITRE) & Alta Planning and Design. February 2018. Evaluating the Economic Impact of Shared Use Paths in North Carolina: 2015-2017 Final Report. [https://itre.ncsu.edu/wp-content/uploads/2018/03/NCDOT-2015-44_SUP-Project_Final-Report_optimized.pdf].

OpenStreetMap. Highways. Data extracted through Geofabrik downloads. Accessed February 23, 2022. [https://wiki.openstreetmap.org/wiki/Highways].

Running USA. 23 March 2017. U.S. Road Race Trends. Road race finisher total experiences slight year-over-year decline in 2016. [https://web.archive.org/web/20170404232619/https://www.runningusa.org/2017-us-road-race-trends].

U.S. Geological Survey (USGS). Published June 2021. National Land Cover Database (NLCD) 2019 Land Cover Conterminous United States. Sioux Falls, SD. [https://doi.org/10.5066/P9KZCM54].

U.S. Department of Transportation. National Highway Traffic Safety Administration and the Bureau of Transportation Statistics. 2002. National Survey of Pedestrian & Bicyclist Attitudes and Behaviors: Highlights Report. [https://www.bts.gov/sites/bts.dot.gov/files/docs/browse-statistical-products-and-data/bts-publications/archive/203331/entire-1.pdf].

Yang, Limin, Jin, Suming, Danielson, Patrick, Homer, Collin G., Gass, L., Bender, S.M., Case, Adam, Costello, C., Dewitz, Jon A., Fry, Joyce A., Funk, M., Granneman, Brian J., Liknes, G.C., Rigge, Matthew B., Xian, George. 2018. A new generation of the United States National Land Cover Database—Requirements, research priorities, design, and implementation strategies: ISPRS Journal of Photogrammetry and Remote Sensing, v. 146, p. 108–123. [https://doi.org/10.1016/j.isprsjprs.2018.09.006].

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

tags['amenity'] IS NOT NULL OR tags['man_made'] IS NOT NULL OR tags['shop'] IS NOT NULL OR tags['tourism'] IS NOT NULL

Features may have these attributes:

• name
• name:en
• amenity
• man_made
• shop
• tourism
• opening_hours
• beds
• rooms
• addr:full
• addr:housenumber
• addr:street
• addr:city
• source
• name:th

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.

Reason for Selection This indicator captures the recreational value and opportunities to connect with nature provided by greenways and trails. Greenways and trails provide many well-established social and economic benefits ranging from improving human health, reducing traffic congestion and air and noise pollution, increasing property values, and generating new jobs and business revenue (ITRE 2018). The locations of greenways and trails are regularly updated through the open-source database OpenStreetMap. Input Data

Southeast Blueprint 2023 subregions: Caribbean
Southeast Blueprint 2023 extent
2012 NOAA Coastal Change Analysis Program (C-CAP) land cover files for the U.S. Virgin Islands (St. Thomas, St. John, and St. Croix are provided as separate rasters), accessed 11-10-2022; learn more about C-CAP high resolution land cover and change products
2010 NOAA C-CAP land cover files for Puerto Rico, accessed 11-10-2022; learn more about C-CAP high resolution land cover and change products
OpenStreetMap data “lines” layer, accessed 2-26-2023 

A line from this dataset is considered a potential greenway/trail if the “highway” tag attribute is either bridleway, cycleway, footway, or path. In OpenStreetMap, a highway refers to “any road, route, way, or thoroughfare on land which connects one location to another and has been paved or otherwise improved to allow travel by some conveyance, including motorized vehicles, cyclists, pedestrians, horse riders, and others (but not trains)”. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more on the OSM copyright page. Mapping Steps The greenways and trails indicator score reflects both the natural condition and connected length of the greenway/trail. Natural condition Natural condition is based on the amount of impervious surface surrounding the greenway/trail. Since perceptions of a greenway’s “naturalness” are influenced both by the immediate surroundings adjacent to the path, and the greater viewshed, natural condition is calculated by averaging two measurements: local impervious and nearby impervious.

Local impervious is defined as the percent impervious surface of the 30 m pixel that intersects the trail. Nearby impervious is defined as the average impervious surface within a 300 m radius circle surrounding the path (note: along a 300 m stretch of trail, we only count the impervious surface within a 45 m buffer on either side of the trail, since pixels nearer the trail have a bigger impact on the greenway/trail experience). The natural classes are defined as follows: 3 = Mostly natural: average of local and nearby impervious is ≤1% 2 = Partly natural: average of local and nearby impervious is >1 and <10% 1 = Developed: average of local and nearby impervious is ≥10%

To create a percent impervious layer, start by converting the C-CAP land cover rasters for Puerto Rico (2 m resolution) and the U.S. Virgin Islands (separate downloads for St. Thomas, St. John, and St. Croix with 2.4 m resolution) from .img format to .tif using the Copy Raster function.
For each individual C-CAP layer, use the ArcPy Conditional function to make a binary raster assigning the impervious class a value of 100 (representing fully impervious) and all other classes a value of 0 (representing fully permeable). This mimics the data format of the 2019 National Land Cover Database (NLCD) used in the continental Southeast permeable surface indicator, which provides a continuous impervious surface value ranging from 0 to 100. Use focal statistics to calculate the percent of cells in a 30 m square that are identified as impervious in the C-CAP data, then reproject and resample the result to a 30 m resolution. 
Use the Cell Statistics “MAX” function to combine the resulting four 30 m C-CAP impervious rasters. This creates an approximation of the percent developed impervious score from the 2019 NLCD.

Connected length The connected length of the path is calculated using the entire extent of the potential greenways/trails dataset. A trail is considered connected to another trail if it is within 2 m of the other trail. Length thresholds are defined by typical lengths of three common recreational greenway activities: walking, running, and biking. The 40 km threshold for biking is based on the standard triathlon biking segment of 40 km (~25 mi). Because a 5K is the most common road race distance, the running threshold is set at 5 km (~3.1 mi) (Running USA 2017). The 1.9 km (1.2 mi) walking threshold is based on the average walking trip on a summer day (U.S. DOT 2002).

Using the statistics software R, download the OpenStreetMap data for Puerto Rico and the US Virgin Islands.
Select all lines from the OpenStreetMap data that have a highway tag of either footway, cycleway, bridleway, or path. These are all considered potential trails. 
Removed all lines marked as private.
Identify lines from the potential trails that are tagged as sidewalks. Assign them a value of 1 in the indicator.

Final scores If the potential greenway/trail was tagged as a sidewalk in the “other tags” field, it is given a value of 1 to separate sidewalks from what most people think of as a trail or greenway. If a pixel does not intersect a potential greenway/trail but is covered by the C-CAP landcover data, it is coded with a value of 0. Clip to the Caribbean Blueprint 2023 subregion. As a final step, clip to the spatial extent of Southeast Blueprint 2023.

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows: 6 = Mostly natural and connected for 5 to <40 km or partly natural and connected for ≥40 km 5 = Mostly natural and connected for 1.9 to <5 km, partly natural and connected for 5 to <40 km, or developed and connected for ≥40 km 4 = Mostly natural and connected for <1.9 km, partly natural and connected for 1.9 to <5 km, or developed and connected for 5 to <40 km 3 = Partly natural and connected for <1.9 km or developed and connected for 1.9 to <5 km 2 = Developed and connected for <1.9 km 1 = Sidewalk 0 = Not identified as a trail, sidewalk, or other path Known Issues

This indicator sometimes misclassifies sidewalks as greenways and trails because they are not tagged as a sidewalk in the OpenStreetMap data.
This indicator occasionally misclassifies driveways as “sidewalks and other paths” in places where they are not correctly tagged as private in OpenStreetMap. These typically appear as isolated pixels receiving a score of 1 on the indicator.
OpenStreetMap does not provide a complete inventory of greenways and trails in the U.S. Caribbean. Paths that are missing from the source data will be underprioritized in this indicator. For example, some trails are missing within National Wildlife Refuges.
This indicator includes trails and sidewalks from OpenStreetMap, which is a crowdsourced dataset. While members of the OpenStreetMap community often verify map features to check for accuracy and completeness, there is the potential for spatial errors (e.g., misrepresenting the path of a greenway) or incorrect tags (e.g., mislabeling a path as a footway that is actually a road for vehicles). However, using a crowdsourced dataset gives on-the-ground experts, Blueprint users, and community members the power to fix errors and add new greenways and trails to improve the accuracy and coverage of this indicator in the future.
This indicator sometimes underestimates greenway length when connections route under bridges or along abandoned dirt roads. Some of these issues have been fixed through active testing and improvement, but some likely remain.
Some greenways and trails continue along roadways that allow motorized vehicles, which are excluded from this indicator. As a result, certain trails may appear incomplete because the indicator only captures the sections dedicated for cyclists, pedestrians, and horseback riders.
When calculating nearby impervious for one greenway, if there’s another greenway within 300 m, impervious surface from the different but overlapping greenway buffer area is also used to compute natural condition. This is an unintended issue with the analysis methods. Investigation into potential fixes is ongoing.
The indicator doesn’t currently include areas where future greenways are planned.
This indicator doesn’t include Mona Island, even though there are important and popular trails, due to the lack of landcover data. 

Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov). Literature Cited American Planning Association. 2018. Recommendations for Future Enhancements to the Blueprint. [https://secassoutheast.org/pdf/Recommendations-for-Future-Enhancements-to-the-Blueprint-FINAL.pdf].

Institute for Transportation Research and Education (ITRE) & Alta Planning and Design. February 2018. Evaluating the Economic Impact of Shared Use Paths in North Carolina: 2015-2017 Final Report. [https://itre.ncsu.edu/wp-content/uploads/2018/03/NCDOT-2015-44_SUP-Project_Final-Report_optimized.pdf].

National Oceanic and Atmospheric Administration, Office for Coastal Management. “C-CAP Land Cover Files for Puerto Rico and US Virgin Islands”. Coastal Change Analysis Program (C-CAP) High-Resolution Land Cover. Charleston, SC: NOAA Office for Coastal Management. Accessed November 2022. [https://www.coast.noaa.gov/htdata/raster1/landcover/bulkdownload/hires/].

OpenStreetMap. Highways. Data extracted through Geofabrik downloads. Accessed February 26,

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

tags['building'] IS NOT NULL

Features may have these attributes:

• name
• name:en
• building
• building:levels
• building:materials
• addr:full
• addr:housenumber
• addr:street
• addr:city
• office
• source
• name:ur

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.