Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

QGIS is a Free and Open Source Geographic Information System. This dataset contains all the information to get you started.

ArcGIS tool and tutorial to convert the shapefiles into network format. The latest version of the tool is available at http://csun.uic.edu/codes/GISF2E.htmlUpdate: we now have added QGIS and python tools. To download them and learn more, visit http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from a workshop that was conducted on October 19-23, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

This dataset has been created to train Univ. Eiffel personnels on raster data handling with QGIS.

It provides the following elements:

• Geopackage database with the following layers:
  • QGIS project
  • Extract from the SENTINEL-2 2022-06-11 B8A band
  • Extract from the SENTINEL-2 2022-06-11 B12 band
  • Extract from the SENTINEL-2 2022-07-21 B8A band
  • Extract from the SENTINEL-2 2022-07-21 B12 band
  • Reclassified delta NBR raster layer
  • Delta NBR vector layer
  • Studied area bounding box
• Intermediate results:
  • pre-event NBR raster file
  • post-event NBR raster file
  • Delta NBR raster file
  • Delta NBR raster file multiplied by 1000 (for easier reclassification)

Data sources IDs from opensearch-theia.cnes.fr-sentinel2-l2a catalogue :

• SENTINEL2B_20220721-104826-811_L2A_T31TFL_D
• SENTINEL2B_20220611-104824-395_L2A_T31TFL_D

Several datasets and workbook for use in the Visualising Arts and Humanities Data Workshop at the FOSS4G UK 2016 conference in Southampton. Tiff data generated from OpenStreetMap in QGIS as a screen Grab. (CC BY_SA). London Local Authorities derived from Open Government Data (OGL). Geoparsed text data derived from a book using the Edinburgh Geoparser, this data has been randomised and annonymised so is open data(ODbl). Hexagons created in QGIS using the MMQGIS plugin and is open data (ODbl). Other. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2016-06-10 and migrated to Edinburgh DataShare on 2017-02-22.

Overview

A map of smallholder-dominated landscapes covering the provinces Niassa, Zambezia, Cabo Delgado, and Nampula in Northern Mozambique. The map includes active cropland and short-term fallows as separate classes, as well as five land cover classes (herbaceous vegetation, open woodlands, closed woodlands, non-vegetated land, water). The map is based on PlanetScope mosaics and consequently comes at 4.77m spatial resolution.

The download contains the following files:

• ps_lc_nmoz.tif / .qml: land cover map and associated QGIS style file
• ps_lc_nmoz_probmargins.tif / .qml: probability margins and associated QGIS style file
• training.gpkg: training samples with class labels
• LICENSE.pdf: NICFI data program user license

Map accuracy

We conducted an area-adjusted accuracy assessment based on a stratified random sample, which yielded important insights regarding accuracies and error types. The area-adjusted overall accuracy of the map is 88.9%, but users should be aware of the most important error types:

• Active cropland were overestimated, whereas local topographical depressions with moist soils, and regions with exposed soils/rocks and sparse vegetation cover were found to be falsely classified.
• Short-term fallows were underestimated, particularly in regions with high growth rates and extensive land management, such as parts of the northern and north-eastern study region.

Further resources

The production of this map was made possible through the NICFI data program, providing the PlanetScope mosaics and the Google Earth Engine cloud computing platform for preprocessing of the satellite data and classification. As such, the use of the map falls under the NICFI data program license agreement included in the download. The code for preprocessing the PlanetScope mosaics is based on the Google Earth Engine Python API and made available at https://github.com/philipperufin/eepypr/.

We advise map users to read the preprint or the open access paper for detailed insights. In case of questions please consult these resources or contact the lead author of the work.

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from a workshop that was conducted on August 17-21, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

Questions we asked in the Georeferencing for Research Follow Up Survey done 3 months after the workshop.

This resource contains the test data for the GeoServer OGC Web Services tutorials for various GIS applications including ArcGIS Pro, ArcMap, ArcGIS Story Maps, and QGIS. The contents of the data include a polygon shapefile, a polyline shapefile, a point shapefile, and a raster dataset; all of which pertain to the state of Utah, USA. The polygon shapefile is of every county in the state of Utah. The polyline is of every trail in the state of Utah. The point shapefile is the current list of GNIS place names in the state of Utah. The raster dataset covers a region in the center of the state of Utah. All datasets are projected to NAD 1983 Zone 12N.

This archive contains geospatial data, as well as the code used to generate the geospatial data.

The geospatial data consists of georeferenced polygons identifying areas which are covered by green roofs in London (GBR) generated from 2019 aerial imagery.

The data is described in detail in the manuscript An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery. See abstract below.

Archive contents:

geospatial\_data/green\_roofs\_220719.geojson is the main result, which can be opened in any GIS program.

segmentation\_code contains the Python code used to produce the segmentation from the aerial imagery.

analysis\_code contains the Python code used to produce the plots and tables for the paper, as well as the OS intersection postprocessing step.

GeoJSON format:

GeoJSON is a format for encoding geospatial data, see https://geojson.org/.

GeoJSON can be read using GIS programs including ArcGIS, QGIS, OGR.

Input data availability:

Unfortunately the aerial imagery and building footprint data cannot be shared directly, as you will require the proper license. Both can be found at Digimap provided your institution has the license.

Abstract:

Green roofs are roofs incorporating a deliberate layer of growing substrate and vegetation. They can reduce both indoor and outdoor temperatures, so are often presented as a strategy to reduce urban overheating, which is expected to increase due to climate change. In addition, they could help decrease the cooling energy demand of buildings thereby contributing to energy and emissions reductions and provide benefits to biodiversity and human well-being. To guide the design of more sustainable and climate resilient buildings and neighbourhoods, there is a need to assess the existing status of green roof coverage and explore the potential for future implementation. Therefore, accurate information on the prevalence and characteristics of existing green roofs is required to estimate any effect of green roofs on temperatures (or other phenomena), but this information is currently lacking. Using a machine-learning algorithm based on U-Net to segment aerial imagery, we surveyed the area and coverage of green roofs in London, producing a geospatial dataset. We estimate that there was 0.19 km^2 of green roof in the Central Activities Zone (CAZ) of London, (0.81 km^2) in Inner London, and (1.25 km^2) in Greater London in the year 2019. This corresponds to 1.6% of the total building footprint area in the CAZ, and 1.0% in Inner London. There is a relatively higher concentration of green roofs in the City of London (the historic financial district), covering 3.1% of the total building footprint area. The survey covers 1463 km^2 of Greater London, making this the largest open automatic survey of green roofs in any city. We improve on previous studies by including more negative examples in the training data, by experimenting with different data augmentation methods, and by requiring coincidence between vector building footprints and green roof patches. This dataset will enable future work examining the distribution and potential of green roofs in London and on urban climate modelling.

Example RGB and Multispectral data for processing with Agisoft Metashape and QGIS to create georeferenced orthomosaics and classifications.

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

These data sets include yearly maps of land cover classification for the state of Mato Grosso, Brazil, from 2001 (2000-09-01 to 2001-08-31) to 2017 (2016-09-01 to 2017-08-31), based on MODIS image time series (collection 6) at 250-meter spatial resolution (product MOD13Q1). Ground samples consisting of 1,892 time series with known labels are used as training data for a support vector machine classifier. We used the radial basis function kernel, with cost C=1 and gamma = 0.01086957. The classes include natural and human-transformed land areas, discriminating among different agricultural crops in state of land cover change maps for Mato Grosso State in Brazil. The results provide spatially explicit estimates of productivity increases in agriculture as well as the trade-offs between crop and pasture expansion. --- The correlation coefficients between the agricultural areas classified by our method and the estimates by IBGE (Brazil's Census Bureau) for the harvests from 2001 to 2017, were equal to 0.98. At the state level the soybean, cotton, corn and sunflower areas had a correlation equal 0.97, 0.85, 0.98 and 0.80. --- The areas classified as forest were compared with the Hansen et al. (2013, doi:10.1126/science.1244693) mapping for the year 2000. In order to separate the forest areas, we examined the areas with more than 25% tree cover on the Hansen et al. (2013, doi:10.1126/science.1244693) map. We found that 98% of the pixels classified as forest match the pixels indicated by Hansen et al. (2013) as having more than 25% tree cover. When we joined the cerrado and forest classes, 83% of the pixels match the pixels by Hansen et al. (2013) as having more than 25% tree cover. --- The pixels labeled as pasture were compared to the pasture mapping done by Parente et al. (2017, doi:10.1016/j.jag.2017.06.003). We found that 80% of the pixels classified as forest match the pixels indicated by Parente et al. (2017, doi:10.1016/j.jag.2017.06.003) for the state of Mato Grosso. --- In the land cover change maps for Mato Grosso State in Brazil version 3, we applied a methodology to deal with trajectories in classified maps. This methodology for reasoning about land-use change trajectories, called LUC Calculus, has been discussed in previous work (Maciel et al., 2018, doi:10.1080/13658816.2018.1520235). For reducing the temporal variability, we use the entire history of the study area considered as a set of land-use trajectories (from 2001 to 2017). For reasoning about this, we adopt the reference date 2001 and we used two-step post-processing, first applying masks and rules on the initial classified map (2001) and then land-use rules using LUC Calculus for the all years (2001-2017). The first-step post-processing was performed on the initial classified map (2001). We applied the forest mask to the classified map of the year 2001. This forest mask comes from the PRODES Project (http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes). In the non-forest, the appearance of secondary vegetation is not mapped. An additional set of rules was applied on the initial map using two sets of maps: PRODES map of the year 2001 and Cerrado map of 2000 (http://www.obt.inpe.br/cerrado). This mask of the Cerrado biome depicts the Cerrado within two classes: Anthropized Cerrado and Non-Anthropized Cerrado. The second-step post-processing was carried on the entire years from the classified map (2001-2017) using the LUC Calculus method. First, we elaborate a set of rules defined by experts in Amazon and Cerrado biomes. These rules express information about different trajectories of land-use change in MT that represent an irregular transition between classes. The rules used was: Forest (F), Cerrado (C), Pasture (P) and Soybean (S) 1. C -> F to C -> C 2. C -> C -> P -> C to C -> C -> C -> C 3. C -> C -> S -> C to C -> C -> C -> C 4. P -> P -> C -> C -> P to P -> P -> P -> P -> P 5. F -> C -> F -> F to F -> F -> F -> F 6. F -> F -> C -> F to F -> F -> F -> F 7. F -> C -> F to F -> F -> F 8. F -> C to F -> F 9. F -> F -> P -> F to F -> F -> P -> SV 10. P -> P -> F -> P to P -> P -> SV -> P The sequential application of the rules is able to ensure the temporal consistency among classes over the years. The class changed is highlighted with "*". From rule 1 to 8 we assume the reference date, 2001, as the starting point to find the class to will be changed. Rules 9 and 10 exemplify scenery where new class secondary vegetation (SV) occurs. The trajectory methodology enables us to include a new class called 'secondary vegetation'. This class represents a significant portion of the deforestation areas that have fallen into disuse or abandoned and have regrown as secondary forest. --- The following data sets are provided: (a) The classified maps in compressed TIFF format (one per year) at MODIS resolution. (b) A QGIS style file for displaying the data in the QGIS software (c) An csv file with the training data set (1,892 ground samples). --- The software used to produce the analysis is available as open source on https://github.com/e-sensing. --- Note: The TIFF raster files use the Sinusoidal Projection, which is the same cartographical projection used by the input MODIS images. When opening the TIFF raster maps in QGIS, to ensure correct navigation please use the Sinusoidal Projection, by selecting in QGIS projection menu, the following option: "Generated CRS (+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs)"

The dataset depicts the authoritative boundaries of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas in the United States and Territories. These sites encompass land which is federally owned or otherwise managed. This dataset was created from source data provided by the four Military Service Component headquarters and was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program within the Office of the Deputy Under Secretary of Defense for Installations and Environment, Business Enterprise Integration Directorate. Sites were selected from the 2010 Base Structure Report (BSR), a summary of the DoD Real Property Inventory. This list does not necessarily represent a comprehensive collection of all Department of Defense facilities, and only those in the fifty United States and US Territories were considered for inclusion. For inventory purposes, installations are comprised of sites, where a site is defined as a specific geographic location of federally owned or managed land and is assigned to military installation. DoD installations are commonly referred to as a base, camp, post, station, yard, center, homeport facility for any ship, or other activity under the jurisdiction, custody, control of the DoD.

© US Department of Defense This layer is sourced from maps.bts.dot.gov.

The dataset depicts the authoritative boundaries of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas in the United States and Territories (NTAD 2015). These sites encompass land which is federally owned or otherwise managed. This dataset was created from source data provided by the four Military Service Component headquarters and was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program within the Office of the Deputy Under Secretary of Defense for Installations and Environment, Business Enterprise Integration Directorate. Sites were selected from the 2010 Base Structure Report (BSR), a summary of the DoD Real Property Inventory. This list does not necessarily represent a comprehensive collection of all Department of Defense facilities, and only those in the fifty United States and US Territories were considered for inclusion. For inventory purposes, installations are comprised of sites, where a site is defined as a specific geographic location of federally owned or managed land and is assigned to military installation. DoD installations are commonly referred to as a base, camp, post, station, yard, center, homeport facility for any ship, or other activity under the jurisdiction, custody, control of the DoD.

© US Department of Defense

The twisted-wing parasite order (Strepsiptera Kirby, 1813) is difficult to study due to the complexity of strepsipteran life histories, small body sizes, and a lack of accessible distribution data for most species. Here, we present a review of the strepsipteran species known from New York State. We also demonstrate successful collection methods and a survey of species carried out in an old-growth deciduous forest dominated by native New York species (Black Rock Forest, Cornwall, NY) and a private site in the Catskill Mountains (Shandaken, NY). Additionally, we model suitable habitat for Strepsiptera in the United States with species distribution modeling. We base our models on host distributions and climatic variables to inform predictions of where these twisted-wing parasites are likely to be found. With this work, we hope to provide a useful reference for the future collection of Strepsiptera. Methods Our specimens were collected in Black Rock Forest (BRF), Cornwall, New York over the course of six trips in July and August of 2022 and 2023. BRF is an old growth forest protected and maintained by a namesake scientific organization dedicated to its study—as such, this forest provides a uniquely mature and native environment in which to collect ecological data. We sampled six areas: native growth by the Black Rock Forest (BRF) Science Center (41.41408°, -74.011919°), a patch of wild growth in the parking lot (41.413249°, -74.011421°), the meadow of the Upper Reservoir (41.411015°, -74.007048°), Aleck Meadow (41.406405°, -74.014587°), meadows of Jim’s Pond (41.387490°, -74.020348°), and brush near the Stone House (41.397177°, -74.021423°) (Figure S1). In addition to the BRF sites, we sampled one privately owned site in the Catskill Mountains, Shandaken, New York in June and July 2023 (42.129425°, -74.377613°). To generate predictive models of host and Strepsiptera ranges, we gathered occurrence data for each host-parasite pair for which collection coordinates were available from the Global Biodiversity Information Facility (GBIF) and combined it with the locality data from our collection efforts. Of the 78 strepsipteran species documented in the United States, only a subset had occurrence data. Of these, 51 species included specific coordinate data, and only 15 species had multiple unique coordinates. If hosts of these strepsipterans did not have occurrence data, we excluded these host species from the predictive analyses as well. Since our models require at least 5 occurrence datapoints to run, we ran models on genera instead of species to ensure that our predictions were robust. Our list was based on a checklist of strepsipteran species and their hosts in the United States from Kathirithamby, 2005, plus a United States checklist (Zabinski & Cook, 2023) and world checklist of the genus Stylops (Straka et al., 2015). Our GBIF search parameters specified human observation and preserved specimens as basis of record, data with coordinates, and the United States as an administrative area to restrict the search. When necessary for lessening computational time, we thinned the data by specifying coordinate uncertainty between 0-1 meters. We took a species distribution modeling approach with the R package “wallace” and its modeling application Wallace v2.0 (Kass et al., 2018, 2023), using the algorithm MaxEnt (Maximum Entropy) (Phillips et al., 2004) and incorporating Bioclim environmental data (Booth et al., 2014) as explanatory variables driving species presence. For each species of Strepsiptera, we incorporated its host presence-absence prediction (10 percentile training presence threshold visualization) as a categorical variable. We standardized our models by specifying their region of study to a shapefile of the 48 contiguous United States, which we generated in QGIS using publicly available data (United States Government, 2023). We chose each model based on corrected Akaike information criterion (AICc), average omission rate when applying a 10-percentile training presence threshold to withheld validation data (OR.10p), and area under the curve of a receiver operating characteristic plot (auc.val.avg) (Kass et al., 2021; Peterson et al., 2011). Our R scripts for each model are openly available at Dryad. We visualized all data resulting from our models in QGIS v3.2.6 (Flenniken et al., 2020), and generated our host-parasite and species richness maps by using the QGIS Raster Calculator addition function.

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from a workshop that was conducted on June 08-09 2022. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.22, as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

This document contains an annotated set of data quality checks that participants report they use when evaluating and cleaning datasets. These items outline how participants are judging if the data suits their purpose.

Summary:

The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016.

These data are associated with an existing GitHub Repository, https://github.com/tnc-ny-science/NYC_GreenRoofMapping, and as needed and appropriate pending future work, versioned updates will be released here.

Terms of Use:

The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data.

Associated Files:

As of this release, the specific files included here are:

• GreenRoofData2016_20180917.geojson is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).
• GreenRoofData2016_20180917.gpkg is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917_Shapefile.zip is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917.csv is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.

Column Information for the datasets:

Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below.

• fid - Unique identifier
• bin - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh. Associated metadata for fields from that dataset are available at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md.
• bbl - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (http://gis.nyc.gov/taxmap/map.htm) and PLUTO/MapPLUTO (https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.
• gr_area - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).
• bldg_area - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).
• prop_gr - Proportion of the building covered by green roof according to this layer (gr_area/bldg_area).
• cnstrct_yr - Year the building was constructed, pulled from the Building Footprint data.
• doitt_id - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.
• heightroof - Height of the roof of the associated building, pulled from the Building Footprint Data.
• feat_code - Code describing the type of building, pulled from the Building Footprint Data.
• groundelev - Lowest elevation at the building level, pulled from the Building Footprint Data.
• qa - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'
• notes - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.
• classified - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)
• digitized - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)
• newlyadded - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)
• original_source - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.
• address - Address based on MapPLUTO, joined to the dataset based on bbl.
• borough - Borough abbreviation pulled from MapPLUTO.
• ownertype - Owner type field pulled from MapPLUTO.
• zonedist1 - Zoning District 1 type pulled from MapPLUTO.
• spdist1 - Special District 1 pulled from MapPLUTO.
• bbl_fixed - Flag to indicate whether bbl was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)

For GreenRoofData2016_20180917.csv there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263):

• xcoord - Longitude in decimal degrees.
• ycoord - Latitude in decimal degrees.

Acknowledgements:

This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.

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