With this add in it is possible to create map templates from GIS files in KML format, and create choropleths with them.

Providing you have access to KML format map boundary files, it is possible to create your own quick and easy choropleth maps in Excel. The KML format files can be converted from 'shape' files. Many shape files are available to download for free from the web, including from Ordnance Survey and the London Datastore. Standard mapping packages such as QGIS (free to download) and ArcGIS can convert the files to KML format.

A sample of a KML file (London wards) can be downloaded from this page, so that users can easily test the tool out.

Macros must be enabled for the tool to function.

When creating the map using the Excel tool, the 'unique ID' should normally be the area code, the 'Name' should be the area name and then if required and there is additional data in the KML file, further 'data' fields can be added. These columns will appear below and to the right of the map. If not, data can be added later on next to the codes and names.

In the add-in version of the tool the final control, 'Scale (% window)' should not normally be changed. With the default value 0.5, the height of the map is set to be half the total size of the user's Excel window.

To run a choropleth, select the menu option 'Run Choropleth' to get this form.

To specify the colour ramp for the choropleth, the user needs to enter the number of boxes into which the range is to be divided, and the colours for the high and low ends of the range, which is done by selecting coloured option boxes as appropriate. If wished, hit the 'Swap' button to change which colours are for the different ends of the range. Then hit the 'Choropleth' button.

The default options for the colours of the ends of the choropleth colour range are saved in the add in, but different values can be selected but setting up a column range of up to twelve cells, anywhere in Excel, filled with the option colours wanted. Then use the 'Colour range' control to select this range, and hit apply, having selected high or low values as wished. The button 'Copy' sets up a sheet 'ColourRamp' in the active workbook with the default colours, which can just be extended or deleted with just a few cells, so saving the user time.

The add-in was developed entirely within the Excel VBA IDE by Tim Lund. He is kindly distributing the tool for free on the Datastore but suggests that users who find the tool useful make a donation to the Shelter charity. It is not intended to keep the actively maintained, but if any users or developers would like to add more features, email the author.

Acknowledgments

Calculation of Excel freeform shapes from latitudes and longitudes is done using calculations from the Ordnance Survey.

This web map draws attention to your thematic content by providing a dark, neutral background with minimal colors, labels, and features. Only key information is represented to provide geographic context, allowing your data to come to the foreground. Open this web map and choose the "Add" button at the top to add your thematic content, or drag and drop your GIS-ready data to the map.This web map uses the World Dark Gray Base map service as its basemap. This web map also contains the World Dark Gray Reference map service to provide labels for selected cities and towns.This dark gray web map supports bright colors and labels for your theme, creating a visually compelling map graphic which helps your reader see the patterns intended. See this blog post for more information on how to use this map.The map shows populated places, water, roads, urban areas, parks, building footprints, and administrative boundaries. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri or any governing authority. This map was compiled by Esri using HERE data, DeLorme basemap layers, MapmyIndia data, and Esri basemap data. The basemap includes boundaries, administrative labels, and major roads worldwide from 1:591M scale to 1:577k scale. More detailed nationwide coverage is included in North America, Africa, South America and Central America, the Middle East, India, Australia, and New Zealand down to the 1:9k scale. Data for select areas of Africa and Pacific Island nations from ~1:288k to ~1:9k was sourced from OpenStreetMap contributors. Specific country list and documentation of Esri's process for including OSM data is available to view.In addition, some of the data in the World Dark Gray Base map service has been contributed by the GIS community. You can contribute your data to this service and have it served by Esri. For details, see the Community Maps Program. For details on data sources in this map service, view the list of Contributors for the World Dark Gray Base map. Note: The light gray basemap is not supported in ArcGIS for Desktop 9.3 or 9.3.1 because it uses the mixed cache format (both JPEG and PNG).

The Residential Schools Locations Dataset in Geodatabase format (IRS_Locations.gbd) contains a feature layer "IRS_Locations" that contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Residential Schools Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites. Access Instructions: there are 47 files in this data package. Please download the entire data package by selecting all the 47 files and click on download. Two files will be downloaded, IRS_Locations.gbd.zip and IRS_LocFields.csv. Uncompress the IRS_Locations.gbd.zip. Use QGIS, ArcGIS Pro, and ArcMap to open the feature layer IRS_Locations that is contained within the IRS_Locations.gbd data package. The feature layer is in WGS 1984 coordinate system. There is also detailed file level metadata included in this feature layer file. The IRS_locations.csv provides the full description of the fields and codes used in this dataset.

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted GIS data cover administrative and postal divisions with up to 6 precision levels: a zip code layer and up to 5 administrative levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Boundaries Database (GIS data, Geospatial data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the GIS data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our geospatial data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All GIS data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

Developed by SOLARGIS (https://solargis.com) and provided by the Global Solar Atlas (GSA), this data resource contains photovoltaic power potential (PVOUT) in kWh/kWp covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: PVOUT - LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 3.6 GB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

Reason for SelectionProtected 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 DataSoutheast Blueprint 2023 subregions: CaribbeanSoutheast Blueprint 2023 extentNational Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) Coastal Relief Model, accessed 11-22-2022Protected Areas Database of the United States (PAD-US) 3.0: VI, PR, and Marine Combined Fee EasementPuerto 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 censusOpenStreetMap data “multipolygons” layer, accessed 3-14-2023A 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 3-8-2023U.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 StepsMost 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 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 <10 acre urban park1 = <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.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 MindThis 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 because the landcover data available in the Caribbean does not assess percent impervious in a comparable way.Disclaimer: Comparing with Older Indicator VersionsThere are numerous problems with using Southeast Blueprint

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains direct normal irradiation (DNI) in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: DNI LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 343.99 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

Aim: Effective management decisions depend on knowledge of species distribution and habitat use. Maps generated from species distribution models are important in predicting previously unknown occurrences of protected species. However, if populations are seasonally dynamic or locally adapted, failing to consider population level differences could lead to erroneous determinations of occurrence probability and ineffective management. The study goal was to model the distribution of a species of special concern, Townsend’s big-eared bats (Corynorhinus townsendii), in California. We incorporate seasonal and spatial differences to estimate the distribution under current and future climate conditions. Methods: We built species distribution models using all records from statewide roost surveys and by subsetting data to seasonal colonies, representing different phenological stages, and to Environmental Protection Agency Level III Ecoregions to understand how environmental needs vary based on these factors. We projected species’ distribution for 2061-2080 in response to low and high emissions scenarios and calculated the expected range shifts. Results: The estimated distribution differed between the combined (full dataset) and phenologically-explicit models, while ecoregion-specific models were largely congruent with the combined model. Across the majority of models, precipitation was the most important variable predicting the presence of C. townsendii roosts. Under future climate scnearios, distribution of C. townsendii is expected to contract throughout the state, however suitable areas will expand within some ecoregions. Main conclusion: Comparison of phenologically-explicit models with combined models indicate the combined models better predict the extent of the known range of C. townsendii in California. However, life history-explicit models aid in understanding of different environmental needs and distribution of their major phenological stages. Differences between ecoregion-specific and statewide predictions of habitat contractions highlight the need to consider regional variation when forecasting species’ responses to climate change. These models can aid in directing seasonally explicit surveys and predicting regions most vulnerable under future climate conditions. Methods Study area and survey data The study area covers the U.S. state of California, which has steep environmental gradients that support an array of species (Dobrowski et al. 2011). Because California is ecologically diverse, with regions ranging from forested mountain ranges to deserts, we examined local environmental needs by modeling at both the state-wide and ecoregion scale, using U.S. Environmental Protection Agency (EPA) Level III ecoregion designations and there are thirteen Level III ecoregions in California (Table S1.1) (Griffith et al. 2016). Species occurrence data used in this study were from a statewide survey of C. townsendii in California conducted by Harris et al. (2019). Briefly, methods included field surveys from 2014-2017 following a modified bat survey protocol to create a stratified random sampling scheme. Corynorhinus townsendii presence at roost sites was based on visual bat sightings. From these survey efforts, we have visual occurrence data for 65 maternity roosts, 82 hibernation roosts (hibernacula), and 91 active-season non-maternity roosts (transition roosts) for a total of 238 occurrence records (Figure 1, Table S1.1). Ecogeographical factors We downloaded climatic variables from WorldClim 2.0 bioclimatic variables (Fick & Hijmans, 2017) at a resolution of 5 arcmin for broad-scale analysis and 30 arcsec for our ecoregion-specific analyses. To calculate elevation and slope, we used a digital elevation model (USGS 2022) in ArcGIS 10.8.1 (ESRI, 2006). The chosen set of environmental variables reflects knowledge on climatic conditions and habitat relevant to bat physiology, phenology, and life history (Rebelo et al. 2010, Razgour et al. 2011, Loeb and Winters 2013, Razgour 2015, Ancillotto et al. 2016). To trim the global environmental variables to the same extent (the state of California), we used the R package “raster” (Hijmans et al. 2022). We performed a correlation analysis on the raster layers using the “layerStats” function and removed variables with a Pearson’s coefficient > 0.7 (see Table 1 for final model variables). For future climate conditions, we selected three general circulation models (GCMs) based on previous species distribution models of temperate bat species (Razgour et al. 2019) [Hadley Centre Global Environment Model version 2 Earth Systems model (HadGEM3-GC31_LL; Webb, 2019), Institut Pierre-Simon Laplace Coupled Model 6th Assessment Low Resolution (IPSL-CM6A-LR; Boucher et al., 2018), and Max Planck Institute for Meteorology Earth System Model Low Resolution (MPI-ESM1-2-LR; Brovkin et al., 2019)] and two contrasting greenhouse concentration trajectories (Shared Socio-economic Pathways (SSPs): a steady decline pathway with CO2 concentrations of 360 ppmv (SSP1-2.6) and an increasing pathway with CO2 reaching around 2,000 ppmv (SSP5-8.5) (IPCC6). We modeled distribution for present conditions future (2061-2080) time periods. Because one aim of our study was to determine the consequences of changing climate, we changed only the climatic data when projecting future distributions, while keeping the other variables constant over time (elevation, slope). Species distribution modeling We generated distribution maps for total occurrences (maternity + hibernacula + transition, hereafter defined as “combined models”), maternity colonies , hibernacula, and transition roosts. To estimate the present and future habitat suitability for C. townsendii in California, we used the maximum entropy (MaxEnt) algorithm in the “dismo” R package (Hijmans et al. 2021) through the advanced computing resources provided by Texas A&M High Performance Research Computing. We chose MaxEnt to aid in the comparisons of state-wide and ecoregion-specific models as MaxEnt outperforms other approaches when using small datasets (as is the case in our ecoregion-specific models). We created 1,000 background points from random points in the environmental layers and performed a 5-fold cross validation approach, which divided the occurrence records into training (80%) and testing (20%) datasets. We assessed the performance of our models by measuring the area under the receiver operating characteristic curve (AUC; Hanley & McNeil, 1982), where values >0.5 indicate that the model is performing better than random, values 0.5-0.7 indicating poor performance, 0.7-0.9 moderate performance and values of 0.9-1 excellent performance (BCCVL, Hallgren et al., 2016). We also measured the maximum true skill statistic (TSS; Allouche, Tsoar, & Kadmon, 2006) to assess model performance. The maxTSS ranges from -1 to +1:values <0.4 indicate a model that performs no better than random, 0.4-0.55 indicates poor performance, (0.55-0.7) moderate performance, (0.7-0.85) good performance, and values >0.80 indicate excellent performance (Samadi et al. 2022). Final distribution maps were generated using all occurrence records for each region (rather than the training/testing subset), and the models were projected onto present and future climate conditions. Additionally, because the climatic conditions of the different ecoregions of California vary widely, we generated separate models for each ecoregion in an attempt to capture potential local effects of climate change. A general rule in species distribution modeling is that the occurrence points should be 10 times the number of predictors included in the model, meaning that we would need 50 occurrences in each ecoregion. One common way to overcome this limitation is through the ensemble of small models (ESMs) (Breiner et al. 2015., 2018; Virtanen et al. 2018; Scherrer et al. 2019; Song et al. 2019) included in ecospat R package (references). For our ESMs we implemented MaxEnt modeling, and the final ensemble model was created by averaging individual bivariate models by weighted performance (AUC > 0.5). We also used null model significance testing with to evaluate the performance of our ESMs (Raes and Ter Steege 2007). To perform null model testing we compared AUC scores from 100 null models using randomly generated presence locations equal to the number used in the developed distribution model. All ecoregion models outperformed the null expectation (p<0.002). Estimating range shifts For each of the three GCMs and each RCP scenario, we converted the probability distribution map into a binary map (0=unsuitable, 1=suitable) using the threshold that maximizes sensitivity and specificity (Liu et al. 2016). To create the final maps for each SSP scenario, we summed the three binary GCM layers and took a consensus approach, meaning climatically suitable areas were pixels where at least two of the three models predicted species presence (Araújo and New 2007, Piccioli Cappelli et al. 2021). We combined the future binary maps (fmap) and the present binary maps (pmap) following the formula fmap x 2 + pmap (from Huang et al., 2017) to produce maps with values of 0 (areas not suitable), 1 (areas that are suitable in the present but not the future), 2 (areas that are not suitable in the present but suitable in the future), and 3 (areas currently suitable that will remain suitable) using the raster calculator function in QGIS. We then calculated the total area of suitability, area of maintenance, area of expansion, and area of contraction for each binary model using the “BIOMOD_RangeSize” function in R package “biomod2” (Thuiller et al. 2021).

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

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. 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 3.0 national 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