This is a GeoJSON version of this dataset- https://zenodo.org/deposit/4593518

This GeoJSON file is derived from the State Game Land (SGL) vector files provided by the state of Pennsylvania (http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=86). A 1 km buffer was added in QGIS.

For additional information, please see https://zenodo.org/deposit/4593788 .

Band of 300 meters from the lagoon shoreline. WMS-WFS service available via client (for example Qgis, GVsig, Udig, Geomedia, ArcGIS) at the address http://webgis.simfvg.it/wms_ppr/bozza-ppr?

Associated Paper: Baulcomb, C., A.M.W. Wilson, and A.P. Barnes. (20xx). Understanding North Sea Fishers: Social Actors, Economics Agents, and Apex Predators. Fish and Fisheries XX: XX - XX. DOI: XXX

Overview of file:

This is an SQlite data file that can be opened in GIS software like QGIS.
This file shows the boundary of the ICES Greater North Sea Ecoregion with a 0.165 decimal degree buffer applied The projection is EPSG:4326 - WGS 84 - Geographic This layer was created and used to identify ports for inclusion in the paper referenced above. This file is being uploaded here to ensure there is transparency regarding this part of the project (as no where else has, or is host to, this file). However, if readers wish they can re-create this file (or a similar version) for themselves by accessing the ICES Ecoregion Boundary shape files (see links below), cropping the shapefiles, applying the desired buffer in QGIS, and exporting that layer as an SQlite file.

The original data file for the ICES Ecoregion Boundary is CC BY 4.0, so this SQlite file is as well.

This (zipped) shapefile is derived from the State Game Land (SGL) vector files provided by the state of Pennsylvania (http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=86). A 1 km buffer was added in QGIS.

For additional information, please see https://zenodo.org/record/4766351

Reason for Selection Protected natural areas in urban environments provide urban residents a nearby place to connect with nature and offer refugia for some species. Because beaches in Puerto Rico and the U.S. Virgin Islands are open to the public, beaches also provide important outdoor recreation opportunities for urban residents, so we include beaches as parks in this indicator. Input Data

Southeast Blueprint 2023 subregions: Caribbean
Southeast Blueprint 2023 extent
National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) Coastal Relief Model, accessed 11-22-2022
Protected Areas Database of the United States (PAD-US) 3.0: VI, PR, and Marine Combined Fee Easement
Puerto Rico Protected Natural Areas 2018 (December 2018 update): Terrestrial and marine protected areas (PACAT2018_areas_protegidasPR_TERRESTRES_07052019.shp, PACAT2018_areas_protegidasPR_MARINAS_07052019.shp) 
2020 Census Urban Areas from the Census Bureau’s urban-rural classification; download the data, read more about how urban areas were redefined following the 2020 census
OpenStreetMap data “multipolygons” layer, accessed 3-14-2023

A polygon from this dataset is considered a park if the “leisure” tag attribute is either “park” or “nature_reserve”, and considered a beach if the value in the “natural” tag attribute is “beach”. OpenStreetMap describes leisure areas as “places people go in their spare time” and natural areas as “a wide variety of physical geography, geological and landcover features”. Data were downloaded in .pbf format and translated ton an ESRI shapefile using R code. 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.

TNC Lands - Public Layer, accessed 3-8-2023
U.S. Virgin Islands beaches layer (separate vector layers for St. Croix, St. Thomas, and St. John) provided by Joe Dwyer with Lynker/the NOAA Caribbean Climate Adaptation Program on 3-3-2023 (contact jdwyer@lynker.com for more information)

Mapping Steps

Most mapping steps were completed using QGIS (v 3.22) Graphical Modeler.
Fix geometry errors in the PAD-US PR data using Fix Geometry. This must be done before any analysis is possible.
Merge the terrestrial PR and VI PAD-US layers.
Use the NOAA coastal relief model to restrict marine parks (marine polygons from PAD-US and Puerto Rico Protected Natural Areas) to areas shallower than 10 m in depth. The deep offshore areas of marine parks do not meet the intent of this indicator to capture nearby opportunities for urban residents to connect with nature.
Merge into one layer the resulting shallow marine parks from marine PAD-US and the Puerto Rico Protected Natural Areas along with the combined terrestrial PAD-US parks, OpenStreetMap, TNC Lands, and USVI beaches. Omit from the Puerto Rico Protected Areas layer the “Zona de Conservación del Carso”, which has some policy protections and conservation incentives but is not formally protected.
Fix geometry errors in the resulting merged layer using Fix Geometry.
Intersect the resulting fixed file with the Caribbean Blueprint subregion.
Process all multipart polygons to single parts (referred to in Arc software as an “explode”). This helps the indicator capture, as much as possible, the discrete units of a protected area that serve urban residents.
Clip the Census urban area to the Caribbean Blueprint subregion.
Select all polygons that intersect the Census urban extent within 1.2 miles (1,931 m). The 1.2 mi threshold is consistent with the average walking trip on a summer day (U.S. DOT 2002) used to define the walking distance threshold used in the greenways and trails indicator. Note: this is further than the 0.5 mi distance used in the continental version of the indicator. We extended it to capture East Bay and Point Udall based on feedback from the local conservation community about the importance of the park for outdoor recreation.
Dissolve all the park polygons that were selected in the previous step.
Process all multipart polygons to single parts (“explode”) again.
Add a unique ID to the selected parks. This value will be used to join the parks to their buffers.
Create a 1.2 mi (1,931 m) buffer ring around each park using the multiring buffer plugin in QGIS. Ensure that “dissolve buffers” is disabled so that a single 1.2 mi buffer is created for each park.
Assess the amount of overlap between the buffered park and the Census urban area using overlap analysis. This step is necessary to identify parks that do not intersect the urban area, but which lie within an urban matrix. This step creates a table that is joined back to the park polygons using the UniqueID.
Remove parks that had ≤2% overlap with the urban areas when buffered. This excludes mostly non-urban parks that do not meet the intent of this indicator to capture parks that provide nearby access for urban residents. Note: In the continental version of this indicator, we used a threshold of 10%. In the Caribbean version, we lowered this to 2% in order to capture small parks that dropped out of the indicator when we extended the buffer distance to 1.2 miles.
Calculate the GIS acres of each remaining park unit using the Add Geometry Attributes function.
Join the buffer attribute table to the previously selected parks, retaining only the parks that exceeded the 2% urban area overlap threshold while buffered. 
Buffer the selected parks by 15 m. Buffering prevents very small parks and narrow beaches from being left out of the indicator when the polygons are converted to raster.
Reclassify the polygons into 7 classes, seen in the final indicator values below. These thresholds were informed by park classification guidelines from the National Recreation and Park Association, which classify neighborhood parks as 5-10 acres, community parks as 30-50 acres, and large urban parks as optimally 75+ acres (Mertes and Hall 1995).
Export the final vector file to a shapefile and import to ArcGIS Pro.
Convert the resulting polygons to raster using the ArcPy Polygon to Raster function. Assign values to the pixels in the resulting raster based on the polygon class sizes of the contiguous park areas.
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 = 75+ acre urban park 5 = >50 to <75 acre urban park 4 = 30 to <50 acre urban park 3 = 10 to <30 acre urban park 2 = 5 to <10 acre urban park 1 = <5 acre urban park 0 = Not identified as an urban park Known Issues

This indicator does not include park amenities that influence how well the park serves people and should not be the only tool used for parks and recreation planning. Park standards should be determined at a local level to account for various community issues, values, needs, and available resources. 
This indicator includes some protected areas that are not open to the public and not typically thought of as “parks”, like mitigation lands, private easements, and private golf courses. While we experimented with excluding them using the public access attribute in PAD, due to numerous inaccuracies, this inadvertently removed protected lands that are known to be publicly accessible. As a result, we erred on the side of including the non-publicly accessible lands.
This indicator includes parks and beaches 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 boundary of a park) or incorrect tags (e.g., labelling an area as a park that is not actually a park). However, using a crowdsourced dataset gives on-the-ground experts, Blueprint users, and community members the power to fix errors and add new parks to improve the accuracy and coverage of this indicator in the future.

Other Things to Keep in Mind

This indicator calculates the area of each park using the park polygons from the source data. However, simply converting those park polygons to raster results in some small parks and narrow beaches being left out of the indicator. To capture those areas, we buffered parks and beaches by 15 m and applied the original area calculation to the larger buffered polygon, so as not to inflate the area by including the buffer. As a result, when the buffered polygons are rasterized, the final indicator has some areas of adjacent pixels that receive different scores. While these pixels may appear to be part of one contiguous park or suite of parks, they are scored differently because the park polygons themselves are not actually contiguous. 
The Caribbean version of this indicator uses a slightly different methodology than the continental Southeast version. It includes parks within a 1.2 mi distance from the Census urban area, compared to 0.5 mi in the continental Southeast. We extended it to capture East Bay and Point Udall based on feedback from the local conservation community about the importance of the park for outdoor recreation. Similarly, this indicator uses a 2% threshold of overlap between buffered parks and the Census urban areas, compared to a 10% threshold in the continental Southeast. This helped capture small parks that dropped out of the indicator when we extended the buffer distance to 1.2 miles. Finally, the Caribbean version does not use the impervious surface cutoff applied in the continental Southeast

Buffer strips from the Lakes pursuant to Legislative Decree 42/2004, Art. 142, Paragraph 1, b. WMS-WFS service available via client (for example Qgis, GVsig, Udig, Geomedia, ArcGIS) at the address http://webgis.simfvg.it/wms_ppr/bozza-ppr?

This brief technical note has been prepared for the SEPA Planning Team to share the layer with Councils and apply riparian corridor principles together with potential/existing geomorphic risk for consideration within new developments and Local Development Plans (LDP). This summary aims to describe how to use the layer and present caveats on how not to use it for the purposes of new developments. 1. Main principlesThe scale of the layer is national for the baseline river network, i.e. those river water bodies with catchment area greater than 10 km2.This layer made use of:ST:TREAM reaches (baseline river network). These reaches show homogeneous sections of rivers in terms of slope, channel width and flow, and therefore energy. These reaches were used to model potential erosion, transport and deposition. River typology layer (baseline river network).Morphological pressures (baseline river network).The assessment was carried out to identify zones where we expect significant channel erosion and deposition (i.e. leading to channel mobility and instability).Reaches of river were assessed as low risk of mobility and removed based on: if the river type was unlikely to significantly adjust laterally: passive meandering, bedrock, step-pool and plane bed. where the ST:REAM modelling suggested that the processes were predominantly transport/transfer (I.e. deposition or erosion was less likely). consideration of where specific morphological pressures would impede channel adjustment, e.g., bank protection on both banks, etc. (to be confirmed). The width of the polygons is based on the channel width. Lateral channel adjustment is proportional to channel width which is linked to Qmed. 2. How to use it The layer is a shapefile/geodatabase that can be opened in QGIS and ArcGIS. The symbology of the layer shows the areas in polygons (no lines) where there is potential geomorphic risk, e.g. significant erosion of banks that can impact a road. There are two main uses for land use planning:It is not advisable to develop land next to those polygons due to the increased risk of the channel adjusting within this zone; and,Consideration of the potential risk posed to existing infrastructure adjacent to the polygons and the implications of this for future infrastructure provision and development. In some cases there may be options to mitigate this risk. However, hard engineering techniques are not recommended unless completely necessary and may increase the risk of erosion in adjacent reaches. Consideration of a riparian corridor within these spaces is a far more beneficial approach as it helps to mitigate this adjustment.Note: this is based on data collected before 2017 and models run in 2013. Therefore, the data should be ground-truthed as changes may have occurred. See sections below: Site assessment Please always assess the risk via fluvial audit (i.e. ground truthing) to identify new pressures, changes, etc. to be considered within the development. The data provided is aimed to help identified geomorphic risk, but this is not 100% reliable, and rivers change overtime, so use only most up-to-date field data. River networksThe polygons are originally following the digital river network produced by the Centre for Ecology and Hydrology (CEH) 1:50k. This does not match the OS Mastermap. See example in Figure 1, in these cases please, assume the risk is in the nearest section of your river network (if you are not using CEH 1:50k). Please, do not assume the polygon is on the right place on your map without previously checking your river network against the approximate centre line of the polygon. Figure 1 OS Master Map and CEH do not match. The dark blue line with the green polygon corresponds to CEH 1.50k, see how it runs parallel to the river in OS MM. The geomorphic risk would be then allocated to the equivalent section of the OS MM (extract of the Back Burn in Fife).Approximate buffersThe width of the geomorphic risks buffers is approximate, a site assessment will help to assess size in relation to local constraints and landscape settings. In some cases the actual width to apply could be wider, or narrower in others. The extent shown in the layer is just an initial estimate. Network is not up-to-dateBe aware that the CEH 1.50k digital river network used for the layer is not up-to-date. This means, the rivers planform (shape) may have changed, and therefore the geomorphic risk has changed and new site assessment must be done. See example in Figure 2, the existing channel in the Lyne Burn (Dunfermline, Rex Park) follows a different route now as it has been restored to a sinuous form through the park. Figure 2 The Lyne Burn has a completely different planform through the Rex Park in Dunfermline. The geomorphic risk polygon is picking the previous existing risk based on a much faster straightened channel. Help identify risks in existing infrastructure within the new development of LDP.The layer can help identify risk for future developments but also geomorphic risk for existing infrastructure that may be associated with new developments. Therefore, this risk should be assessed for both what has already been built and the proposed new development. By considering both, it can help mitigate future impacts. See Figure 3, example in the River Nith, where there is geomorphic risk near roads in different locations. Figure 3 See geomorphic risk near potentially impacting different roads (overlapping roads). These sections may require special attention and site visits to confirm if mitigation is required. (River Nith, North of Dumfries). 4 July 2022SEPA Senior Hydromorphologists

Reason for SelectionProtected natural areas in urban environments provide urban residents a nearby place to connect with nature and offer refugia for some species. They help foster a conservation ethic by providing opportunities for people to connect with nature, and also support ecosystem services like offsetting heat island effects (Greene and Millward 2017, Simpson 1998), water filtration, stormwater retention, and more (Hoover and Hopton 2019). In addition, parks, greenspace, and greenways can help improve physical and psychological health in communities (Gies 2006). Urban park size complements the equitable access to potential parks indicator by capturing the value of existing parks.Input DataSoutheast Blueprint 2024 extentFWS National Realty Tracts, accessed 12-13-2023Protected Areas Database of the United States(PAD-US):PAD-US 3.0national geodatabase -Combined Proclamation Marine Fee Designation Easement, accessed 12-6-20232020 Census Urban Areas from the Census Bureau’s urban-rural classification; download the data, read more about how urban areas were redefined following the 2020 censusOpenStreetMap data “multipolygons” layer, accessed 12-5-2023A polygon from this dataset is considered a beach if the value in the “natural” tag attribute is “beach”. Data for coastal states (VA, NC, SC, GA, FL, AL, MS, LA, TX) were downloaded in .pbf format and translated to an ESRI shapefile using R code. OpenStreetMap® is open data, licensed under theOpen Data Commons Open Database License (ODbL) by theOpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more onthe OSM copyright page.2021 National Land Cover Database (NLCD): Percentdevelopedimperviousness2023NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of America), 4 (Central Gulf of America), and 5 (Western Gulf of America), accessed 3-27-2024Mapping StepsCreate a seamless vector layer to constrain the extent of the urban park size indicator to inland and nearshore marine areas <10 m in depth. The deep offshore areas of marine parks do not meet the intent of this indicator to capture nearby opportunities for urban residents to connect with nature. Shallow areas are more accessible for recreational activities like snorkeling, which typically has a maximum recommended depth of 12-15 meters. This step mirrors the approach taken in the Caribbean version of this indicator.Merge all coastal relief model rasters (.nc format) together using QGIS “create virtual raster”.Save merged raster to .tif and import into ArcPro.Reclassify the NOAA coastal relief model data to assign areas with an elevation of land to -10 m a value of 1. Assign all other areas (deep marine) a value of 0.Convert the raster produced above to vector using the “RasterToPolygon” tool.Clip to 2024 subregions using “Pairwise Clip” tool.Break apart multipart polygons using “Multipart to single parts” tool.Hand-edit to remove deep marine polygon.Dissolve the resulting data layer.This produces a seamless polygon defining land and shallow marine areas.Clip the Census urban area layer to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Clip PAD-US 3.0 to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Remove the following areas from PAD-US 3.0, which are outside the scope of this indicator to represent parks:All School Trust Lands in Oklahoma and Mississippi (Loc Des = “School Lands” or “School Trust Lands”). These extensive lands are leased out and are not open to the public.All tribal and military lands (“Des_Tp” = "TRIBL" or “Des_Tp” = "MIL"). Generally, these lands are not intended for public recreational use.All BOEM marine lease blocks (“Own_Name” = "BOEM"). These Outer Continental Shelf lease blocks do not represent actively protected marine parks, but serve as the “legal definition for BOEM offshore boundary coordinates...for leasing and administrative purposes” (BOEM).All lands designated as “proclamation” (“Des_Tp” = "PROC"). These typically represent the approved boundary of public lands, within which land protection is authorized to occur, but not all lands within the proclamation boundary are necessarily currently in a conserved status.Retain only selected attribute fields from PAD-US to get rid of irrelevant attributes.Merged the filtered PAD-US layer produced above with the OSM beaches and FWS National Realty Tracts to produce a combined protected areas dataset.The resulting merged data layer contains overlapping polygons. To remove overlapping polygons, use the Dissolve function.Clip the resulting data layer to the inland and nearshore extent.Process all multipart polygons (e.g., separate parcels within a National Wildlife Refuge) to single parts (referred to in Arc software as an “explode”).Select all polygons that intersect the Census urban extent within 0.5 miles. We chose 0.5 miles to represent a reasonable walking distance based on input and feedback from park access experts. Assuming a moderate intensity walking pace of 3 miles per hour, as defined by the U.S. Department of Health and Human Service’s physical activity guidelines, the 0.5 mi distance also corresponds to the 10-minute walk threshold used in the equitable access to potential parks indicator.Dissolve all the park polygons that were selected in the previous step.Process all multipart polygons to single parts (“explode”) again.Add a unique ID to the selected parks. This value will be used in a later step to join the parks to their buffers.Create a 0.5 mi (805 m) buffer ring around each park using the multiring plugin in QGIS. Ensure that “dissolve buffers” is disabled so that a single 0.5 mi buffer is created for each park.Assess the amount of overlap between the buffered park and the Census urban area using “overlap analysis”. This step is necessary to identify parks that do not intersect the urban area, but which lie within an urban matrix (e.g., Umstead Park in Raleigh, NC and Davidson-Arabia Mountain Nature Preserve in Atlanta, GA). This step creates a table that is joined back to the park polygons using the UniqueID.Remove parks that had ≤10% overlap with the urban areas when buffered. This excludes mostly non-urban parks that do not meet the intent of this indicator to capture parks that provide nearby access for urban residents. Note: The 10% threshold is a judgement call based on testing which known urban parks and urban National Wildlife Refuges are captured at different overlap cutoffs and is intended to be as inclusive as possible.Calculate the GIS acres of each remaining park unit using the Add Geometry Attributes function.Buffer the selected parks by 15 m. Buffering prevents very small and narrow parks from being left out of the indicator when the polygons are converted to raster.Reclassify the parks based on their area into the 7 classes seen in the final indicator values below. These thresholds were informed by park classification guidelines from the National Recreation and Park Association, which classify neighborhood parks as 5-10 acres, community parks as 30-50 acres, and large urban parks as optimally 75+ acres (Mertes and Hall 1995).Assess the impervious surface composition of each park using the NLCD 2021 impervious layer and the Zonal Statistics “MEAN” function. Retain only the mean percent impervious value for each park.Extract only parks with a mean impervious pixel value <80%. This step excludes parks that do not meet the intent of the indicator to capture opportunities to connect with nature and offer refugia for species (e.g., the Superdome in New Orleans, LA, the Astrodome in Houston, TX, and City Plaza in Raleigh, NC).Extract again to the inland and nearshore extent.Export the final vector file to a shapefile and import to ArcGIS Pro.Convert the resulting polygons to raster using the ArcPy Feature to Raster function and the area class field.Assign a value of 0 to all other pixels in the Southeast Blueprint 2024 extent not already identified as an urban park in the mapping steps above. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools.Use the land and shallow marine layer and “extract by mask” tool to save the final version of this indicator.Add color and legend to raster attribute table.As a final step, clip to the spatial extent of Southeast Blueprint 2024.Note: For more details on the mapping steps, code used to create this layer is available in theSoutheast Blueprint Data Downloadunder > 6_Code.Final indicator valuesIndicator values are assigned as follows:6= 75+ acre urban park5= 50 to <75 acre urban park4= 30 to <50 acre urban park3= 10 to <30 acre urban park2=5 to <10acreurbanpark1 = <5 acre urban park0 = Not identified as an urban parkKnown IssuesThis indicator does not include park amenities that influence how well the park serves people and should not be the only tool used for parks and recreation planning. Park standards should be determined at a local level to account for various community issues, values, needs, and available resources.This indicator includes some protected areas that are not open to the public and not typically thought of as “parks”, like mitigation lands, private easements, and private golf courses. While we experimented with excluding them using the public access attribute in PAD, due to numerous inaccuracies, this inadvertently removed protected lands that are known to be publicly accessible. As a result, we erred on the side of including the non-publicly accessible lands.The NLCD percent impervious layer contains classification inaccuracies. As a result, this indicator may exclude parks that are mostly natural because they are misclassified as mostly impervious. Conversely, this indicator may include parks that are mostly impervious because they are misclassified as mostly

Urban areas are expanding rapidly, with the majority of the global and US population inhabiting them. Urban forests are critically important for providing ecosystem services to the growing urban populace, but their health is threatened by invasive insects. Insect density and damage are highly variable in different sites across urban landscapes, such that trees in some sites experience outbreaks and are severely damaged while others are relatively unaffected. To protect urban forests against damage from invasive insects and support future delivery of ecosystem services, we must first understand the factors that affect insect density and damage to their hosts across urban landscapes. This study explores how a variety of environmental factors that vary across urban habitats influence density of invasive insects. Specifically, we evaluate how vegetational complexity, distance to buildings, impervious surface, canopy temperature, host availability, and density of co-occurring herbivores impact three invasive pests of elm trees: the elm leaf beetle (Xanthogaleruca luteola), the elm flea weevil (Orchestes steppensis), and the elm leafminer (Fenusa ulmi). Except for building distance, all environmental factors were associated with density of at least one pest species. Furthermore, insect responses to these factors were species-specific, with direction and strength of associations influenced by insect life history. These findings can be used to inform future urban pest management and tree care efforts, making urban forests more resilient in an era where globalization and climate change make them particularly vulnerable to attack. Keywords: urban forest, invasive species, impervious surface, temperature, species interactions. Methods Insect Density At each sampling period, we measured insect density on four branches of each tree, one branch in each cardinal direction (N, S, E, and W). The sampling unit was a 30 cm terminal branch (Dahlsten et al., 1993; Rodrigo et al., 2019), and we assumed equal leaf area per branch. All sampled branches were in the lower canopy up to 3 meters from the ground, and branches that could not be reached from the ground were accessed using a ladder. Sampled branches were haphazardly chosen from a distance where insects were not distinguishable to avoid sampling bias. On each tree branch, we counted individuals of each observable insect stage: beetle eggs, larvae, and adults (the beetle pupates in cryptic locations such as under bark or in the soil, and thus pupae were not counted); weevil leaf mines and adults; and the number of leaves with leafminer mines. Individual leafminer mines were not counted because adult females lay multiple eggs per leaf, and it is common for mines to merge and become indistinguishable from one another as larvae develop. Thus, it was not possible to count the number of individual mines for this species. Leafminer adults were not counted because this stage had disappeared for the season by the start of the first sampling period. The total number of leaves on each branch was also recorded. In addition to serving as the response variable for our environmental hypotheses, insect density of each species was also used as predictor variables for the co-occurring herbivore hypothesis. Tree 0 indicates the end of the dataset. Urban Site Factors Host Availability (AllElm_Density) We measured host availability digitally by counting the number of elm trees within a 100 meter buffer around each tree using QGIS version 3.10.12 (QGIS Development Team, 2022) and a dataset of publicly managed trees provided by municipal forestry departments. We chose a 100 meter radius because significant changes in insect density are detectable for multiple insect species at this spatial scale (Sperry et al., 2001). Although Siberian elm is a preferred host of the insects in this system, other species of elm may also serve as hosts and were thus included in this data set. Following digital assessment, we verified all counts in situ to capture any visible privately owned trees and verify that trees in the dataset were still alive and present in the field. Despite efforts to avoid spatial autocorrelation, four trees had 100 meter buffers that overlapped with the buffer of another tree (that is, two locations where two trees had overlapping buffers). Because the maximum overlap was <14% of the buffer area, we retained these trees in our analyses. Vegetational Complexity (SCI_0_500) We measured the structural complexity of the vegetation in a 10 x 10 meter area around each tree following Shrewsbury & Raupp (2000, 2006). Specifically, we sectioned off a 10 x 10 meter area around each study tree and divided this area into one hundred 12 meter plots. In each of these plots, we recorded five vegetation categories: ground cover (e.g., mulch or turf grass), herbaceous plants (e.g., garden annuals/perennials, tall native grasses), shrubs (e.g., hydrangea, boxwood, barberry), understory trees (e.g., juniper, plum, crabapple, small Siberian Elm), or overstory trees (those with mature canopy including ash, pine, and other elm). One point was awarded for each vegetation type present, resulting in 0-5 points awarded in each plot. To quantify complexity of the vegetation in a continuous way, points were summed for all one hundred plots. Thus, each tree received a vegetational complexity score between 0 and 500. Building Distance (Building Distance_m) To assess the local availability of structures for insect overwintering, we measured the distance of each sampled tree to the nearest building in meters as in Speight et al (1998). This was performed digitally using QGIS version 3.10.12 (QGIS Development Team, 2022) and the ESRI Standard Basemap, which displays built structures. Impervious Surface (ImperviousSurface_20m) Impervious surface data were obtained through the USGS Multi-Resolution Land Characteristics Consortium (Dewitz & US Geological Survey, 2021) on a 30 x 30 meter scale and processed using QGIS version 3.10.12 (QGIS Development Team, 2022). We used the zonal statistics tool to calculate the percentage of impervious surface within a 20 meter buffer surrounding each sampled tree, which is more predictive of herbivorous insect density than impervious surface at larger spatial scales (Just et al., 2019). Although impervious surface data were not available at a smaller spatial scale, the zonal statistics tool allowed us to obtain an estimate of impervious surface within 20 meters of each tree using 30 x 30 meter data by computing an average impervious surface value based on weighted averages of the extent to which each 30 x 30 meter pixel overlapped with the 20 meter buffer around a tree. Canopy Temperature (MeanTemp_Night) Canopy temperature at each tree was measured every 1.5 hours via the iButton Thermochron (model DS1921G-F5). Temperature logging began at 7:30AM MST on June 12 and ended at 7:30AM MST on August 25 for a total of 1,185 data points per logger. We placed each logger in a compostable container to prevent contact with direct sunlight and attached them with a zip tie to branches approximately 2-3 meters from the ground. We placed temperature loggers on the east side of the tree wherever possible or on the west side of the tree if a stable eastern location was not available. Despite efforts to minimize contact with direct sunlight, several loggers recorded artificially inflated temperatures. This made mean and maximum temperatures impractical for analysis. We used mean nighttime temperature in the following analyses (7:30PM-7:30AM MST, n=666 measurements per logger) because the urban heat island effect is less variable, occurs more frequently, and is more intense in urban canopies at night compared to the day (Du et al., 2021; Sun et al., 2019).

This data set contains health and social data that were collected by Bus santé, a cross-sectional population-based study that collects information on cardiovascular risk factors. Using QGIS, a layer containing a 100 m buffer zone around each individual was made to have an estimate of the area inside which each person lives.
Subsequently, in order to obtain one file with both the NDVI and the health information, it was necessary to compute intersection between the hectometric NDVI file and the buffer layer: the median NDVI value inside the buffer zone for each person was calculated.

Summary:

This repository contains spatial data files representing the density of vegetation cover within a 200 meter radius of points on a grid across the land area of New York City (NYC), New York, USA based on 2017 six-inch resolution land cover data, as well as SQL code used to carry out the analysis. The 200 meter radius was selected based on a study led by researchers at the NYC Department of Health and Mental Hygiene, which found that for a given point in the city, cooling benefits of vegetation only begin to accrue once the vegetation cover within a 200 meter radius is at least 32% (Johnson et al. 2020). The grid spacing of 100 feet in north/south and east/west directions was intended to provide granular enough detail to offer useful insights at a local scale (e.g., within a neighborhood) while keeping the amount of data needed to be processed for this manageable.

The contained files were developed by the NY Cities Program of The Nature Conservancy and the NYC Environmental Justice Alliance through the Just Nature NYC Partnership. Additional context and interpretation of this work is available in a blog post.

References:

Johnson, S., Z. Ross, I. Kheirbek, and K. Ito. 2020. Characterization of intra-urban spatial variation in observed summer ambient temperature from the New York City Community Air Survey. Urban Climate 31:100583. https://doi.org/10.1016/j.uclim.2020.100583

Files in this Repository:

Spatial Data (all data are in the New York State Plane Coordinate System - Long Island Zone, North American Datum 1983, EPSG 2263):

Points with unique identifiers (fid) and data on proportion tree canopy cover (prop_canopy), proportion grass/shrub cover (prop_grassshrub), and proportion total vegetation cover (prop_veg) within a 200 meter radius (same data made available in two commonly used formats, Esri File GeoDatabase and GeoPackage):

nyc_propveg2017_200mbuffer_100ftgrid_nowater.gdb.zip

nyc_propveg2017_200mbuffer_100ftgrid_nowater.gpkg

Raster Data with the proportion total vegetation within a 200 meter radius of the center of each cell (pixel centers align with the spatial point data)

nyc_propveg2017_200mbuffer_100ftgrid_nowater.tif

Computer Code:

Code for generating the point data in PostgreSQL/PostGIS, assuming the data sources listed below are already in a PostGIS database.

nyc_point_buffer_vegetation_overlay.sql

Data Sources and Methods:

We used two openly available datasets from the City of New York for this analysis:

Borough Boundaries (Clipped to Shoreline) for NYC, from the NYC Department of City Planning, available at https://www.nyc.gov/site/planning/data-maps/open-data/districts-download-metadata.page

Six-inch resolution land cover data for New York City as of 2017, available at https://data.cityofnewyork.us/Environment/Land-Cover-Raster-Data-2017-6in-Resolution/he6d-2qns

All data were used in the New York State Plane Coordinate System, Long Island Zone (EPSG 2263). Land cover data were used in a polygonized form for these analyses.

The general steps for developing the data available in this repository were as follows:

Create a grid of points across the city, based on the full extent of the Borough Boundaries dataset, with points 100 feet from one another in east/west and north/south directions

Delete any points that do not overlap the areas in the Borough Boundaries dataset.

Create circles centered at each point, with a radius of 200 meters (656.168 feet) in line with the aforementioned paper (Johnson et al. 2020).

Overlay the circles with the land cover data, and calculate the proportion of the land cover that was grass/shrub and tree canopy land cover types. Note, because the land cover data consistently ended at the boundaries of NYC, for points within 200 meters of Nassau and Westchester Counties, the area with land cover data was smaller than the area of the circles.

Relate the results from the overlay analysis back to the associated points.

Create a raster data layer from the point data, with 100 foot by 100 foot resolution, where the center of each pixel is at the location of the respective points. Areas between the Borough Boundary polygons (open water of NY Harbor) are coded as "no data."

All steps except for the creation of the raster dataset were conducted in PostgreSQL/PostGIS, as documented in nyc_point_buffer_vegetation_overlay.sql. The conversion of the results to a raster dataset was done in QGIS (version 3.28), ultimately using the gdal_rasterize function.

Behavioral responses of crows to anthropogenic cues for food (Experiment 1 & 2)

Data for Corvus brachrhynchos behavioral responses to food and anthropogenic objects/ cues. Experiments were conducted along an urban-rural gradient in Oneida County, NY, USA.

Contact Andrea Townsend ([aktownse@hamilton.edu] with any questions. This manuscript has been accepted for publication

Merz, M. R., S. Cote, R. Weinberg, T. Malley and A. K. Townsend. "Can I have fries with that? Context-dependent foraging behavior in urban and rural American crows." Behavioral Ecology, Volume 36, Issue 1, January/February 2025, arae098, https://doi.org/10.1093/beheco/arae098

Data files include:

Data files include:

File: experiment1_ImperviousSurface_TrashBin.csv

Variables

Treatment_Type: whether or not a trash can is present (Y/N)

Percent_impervious_50m: The percentage of impervious surface within a 50m radius around each test site...

Buffer strips of watercourses calculated for sections of type A and type C at 150 m from the fluvial branch, for sections B at 150 m from the edges of the areas identified as watercourses. WMS-WFS service available via client (for example Qgis, GVsig, Udig, Geomedia, ArcGIS) at the address http://webgis.simfvg.it/wms_ppr/bozza-ppr?

Band of 300 meters from the seashore line. WMS-WFS service available via client (for example Qgis, GVsig, Udig, Geomedia, ArcGIS) at the address http://webgis.simfvg.it/wms_ppr/bozza-ppr?

Jeu de données vectorielles surfaciques restituant les zones portuaires en tant que zone tampon de 1000 m autour des principaux ouvrages d'accostage (quais et appontements) dans les ports de commerce de la Caraïbe en 2022. Les ouvrages ont été photo-interprétés sur fonds Google Earth, BingMap et OpenStreetMap dans Qgis pour une échelle d'utilisation du 1/10 000.

This dataset is a 1:250000 major disaster susceptibility zoning (2023) for the 10km buffer zone on both sides of the permafrost section of high-grade highways in the Qinghai Tibet region, with a scale of 1:250000. The data format is raster. This dataset combines factors such as elevation, slope, aspect, surface curvature, terrain humidity index, normalized vegetation index, annual average rainfall, distance from rivers, land use, fine-grained soil content, active layer thickness, thawing index, underground ice content, and solar radiation. Random forest, support vector machine, and logistic regression are used to select the optimal method to calculate the susceptibility index of landslides, debris flows, thermal meltwater landslides, thermal meltwater mudflows, and thermal meltwater lakes and ponds in the buffer zone, and they are fused into gravity type geological hazards (landslides, debris flows) and thermal meltwater type geological hazards (thermal meltwater landslides, thermal meltwater lakes and ponds). Ther - represents thermal meltwater type geological hazards, and Grav - represents gravity type geological hazards. Susceptibility can be divided into five categories: very low represents the extremely low susceptibility area, low represents the low susceptibility area, medium represents the medium susceptibility area, high represents the high susceptibility area, and very high represents the extremely high susceptibility area. And field verification has been conducted, and the verification results are reliable. Grid data can be opened for mapping using software such as ArcGIS, QGIS, Envi, etc. It is recommended to use WGS84 for the data coordinate system.

Kuwait's arid desert landscape, geological formations, and extreme climate conditions make it a potential site for establishing a terrestrial Mars analog, as this research presents a new GIS-based methodology. The Analog Conjunctive Method (ACM) was specifically developed to identify a suitable location in Kuwait to hold a terrestrial Mars analog using a geographic information system (GIS) and remote sensing techniques. Analogs play a crucial role in simulating different Martian conditions, supporting astronaut training, testing various exploration technologies, and doing different types of scientific research on these environments. The ACM method integrates GIS and remote sensing techniques to evaluate the study area, resulting in potential sites for analog. The analysis employs two stages to finalize the best location. In stage one, the newly developed ACM is applied; it systematically eliminates unstable areas while allowing minimal flexibility for real-world environmental adjustment, particularly in regions with natural wind barriers. ACM is used to process the buffers created for the seven criteria (urban areas and farms, coastal areas, streets, airports, oil fields, natural reserves, and country borders) in QGIS to exclude unsuitable areas. Stage two screens the stage one map locations using different data (STRM, Copernicus sentinel-2, and field visits) to polish the selection based on other criteria (water bodies, dust rate, vegetation cover, and topography). The result shows nine locations in Jal Al-Zor as potential analog sites where a random location is selected for a 3D model creation to visualize the analog. Java Mission-planning and Analysis for Remote Sensing (JMARS) software was used to identify similarities between specific areas, such as the Jal Al-Zor escarpment and Huwaimllyah sand dunes in the Kuwait desert, and comparable terrains on Mars. The research concluded that Jal Al-Zor holds substantial potential as a terrestrial Mars analog site due to its geological and topographical similarities to Martian landscapes. This makes it an ideal location for crew training, Mars equipment testing, and further research in Mars analog studies, providing valuable insights for future planetary exploration.

Questo dataset contiene le aree allagate corredate della stima di metri quadrati e metri cubi di acqua caduti (derivati ​​da DTM) determinati sulla base dei rilievi effettuati dai volontari aderenti all'iniziative di citizen science. Il rilievo avviene utilizzando l'app QField da dispositivo mobile dopo eventi atmosferici particolarmente importanti. Il dataset è generato attraverso uno script Python ( https://github.com/USAGEHub/CS_floods) eseguito all'interno del software QGis partendo da elementi lineari (linestring) semplificati. Ciascuna area viene creata utilizzando l'operazione buffer (sulla base della larghezza indicata dall'utente). Gli elementi rilevati nello stesso giorno e che presentano una sovrapposizione spaziale vengono accorpati. - Il dataset è generato attraverso uno script Python ( https://github.com/USAGEHub/CS_floods) eseguito all'interno del software QGis partendo da elementi linestring semplificati. Ciascuna area viene creata utilizzando l'operazione buffer (sulla base della larghezza indicata dall'utente). Gli elementi rilevati nello stesso giorno e che presentano una sovrapposizione spaziale vengono accorpati. Il dataset contiene geometrie poligonali s con la seguente struttura dati: Giorno: giorno del rilievo; Area_m2: superficie allagata; Q_min: quota minima DTM della superficie allagata misurata in metri; Q_max: quota massima del DTM della superficie allagata misurata in m; Diff_q la profondità stimata del volume allagato in cm; Stima_v: volume d'acqua stimato in metri cubi

This data is potential major landslide disaster data for the 10km buffer zone along the Gonghe Yushu section of the Qinghai Tibet High level Highway; This data is based on the IPTA InSAR method to monitor the interannual deformation of the surface within a 10 km buffer zone along the Gonghe Yushu section of the high-grade highway from January 2022 to December 2022. Using software such as QGIS and Google Earth, deformation rate maps, optical remote sensing images, and mountain shadows were overlaid and visually interpreted to preliminarily identify areas with high deformation anomalies. Afterwards, by combining field verification with high-resolution images, high-precision digital elevation models, and real-world 3D models obtained from unmanned aerial vehicle (UAV) airborne radar, the geomorphological and surface deformation characteristics of typical unstable slopes in the study area were investigated, and this data file was finally formed. The verification results are reliable. This data includes the location, type, area, slope, aspect, degree of surface fragmentation, triggering factors, maximum deformation rate, and deformation trend of potential major disasters in the 10km buffer zone along the Gonghe Yushu section; This data has broad application prospects for geological hazard evaluation and prediction, engineering disaster prevention and reduction along the Qinghai Tibet High level Highway Gonghe Yushu.