WorldClim 2.1 provides downscaled estimates of climate variables as monthly means over the period of 1970-2000 based on interpolated station measurements. Here we provide analytical image services of precipitation for each month along with an annual mean. Each time step is accessible from a processing template.Time Extent: Monthly/Annual 1970-2000Units: mm/monthCell Size: 2.5 minutes (~5 km)Source Type: StretchedPixel Type: 16 Bit IntegerData Projection: GCS WGS84Mosaic Projection: GCS WGS84Extent: GlobalSource: WorldClim v2.1Using Processing Templates to Access TimeThere are 13 processing templates applied to this service, each providing access to the 12 monthly and 1 annual mean precipitation layers. To apply these in ArcGIS Online, select the Image Display options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left-hand menu. From the Processing Template pull down menu, select the version to display.What can you do with this layer?This layer may be added to maps to visualize and quickly interrogate each pixel value. The pop-up provides a graph of the time series along with the calculated annual mean value.This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro and an area count of precipitation may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from month to month to show seasonal patterns.To calculate precipitation by land area, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Source Data: The datasets behind this layer were extracted from GeoTIF files produced by WorldClim at 2.5 minutes resolution. The mean of the 12 GeoTIFs was calculated (annual), and the 13 rasters were converted to Cloud Optimized GeoTIFF format and added to a mosaic dataset.Citation: Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. were derived from the NVC. NatureServe developed a preliminary list of potential vegetation types. These data were combined with existing plot data (Cully 2002) to derive an initial list of potential types. Additional data and information were gleaned from a field visit and incorporated into the final list of map units. Because of the park’s small size and the large amount of field data, the map units are equivalent to existing vegetation associations or local associations/descriptions (e.g., Prairie Dog Colony). In addition to vegetation type, vegetation structures were described using three attributes: height, coverage density, and coverage pattern. In addition to vegetation structure and context, a number of attributes for each polygon were stored in the associated table within the GIS database. Many of these attributes were derived from the photointerpretation; others were calculated or crosswalked from other classifications. Table 2.7.2 shows all of the attributes and their sources. Anderson Level 1 and 2 codes are also included (Anderson et al. 1976). These codes should allow for a more regional perspective on the vegetation types. Look-up tables for the names associated with the codes is included within the geodatabase and in Appendix D. The look-up tables contain all the NVC formation information as well as alliance names, unique IDs, and the ecological system codes (El_Code) for the associations. These El_Codes often represent a one-to-many relationship; that is, one association may be related to more than one ecological system. The NatureServe conservation status is included as a separate item. Finally, slope (degrees), aspect, and elevation were calculated for each polygon label point using a digital elevation model and an ArcView script. The slope figure will vary if one uses a TIN (triangulated irregular network) versus a GRID (grid-referenced information display) for the calculation (Jenness 2005). A grid was used for the slope figure in this dataset. Acres and hectares were calculated using XTools Pro for ArcGIS Desktop.

Rising sea levels (SLR) will cause coastal groundwater to rise in many coastal urban environments. Inundation of contaminated soils by groundwater rise (GWR) will alter the physical, biological, and geochemical conditions that influence the fate and transport of existing contaminants. These transformed products can be more toxic and/or more mobile under future conditions driven by SLR and GWR. We reviewed the vulnerability of contaminated sites to GWR in a US national database and in a case comparison with the San Francisco Bay region to estimate the risk of rising groundwater to human and ecosystem health. The results show that 326 sites in the US Superfund program may be vulnerable to changes in groundwater depth or flow direction as a result of SLR, representing 18.1 million hectares of contaminated land. In the San Francisco Bay Area, we found that GWR is predicted to impact twice as much coastal land area as inundation from SLR alone, and 5,297 state-managed sites of contamination may be vulnerable to inundation from GWR in a 1-meter SLR scenario. Increases of only a few centimeters of elevation can mobilize soil contaminants, alter flow directions in a heterogeneous urban environment with underground pipes and utility trenches, and result in new exposure pathways. Pumping for flood protection will elevate the salt water interface, changing groundwater salinity and mobilizing metals in soil. Socially vulnerable communities are more exposed to this risk at both the national scale and in a regional comparison with the San Francisco Bay Area. Methods Data Dryad This data set includes data from the California State Water Resources Control Board (WRCB), the California Department of Toxic Substances Control (DTSC), the USGS, the US EPA, and the US Census. National Assessment Data Processing: For this portion of the project, ArcGIS Pro and RStudio software applications were used. Data processing for superfund site contaminants in the text and supplementary materials was done in RStudio using R programming language. RStudio and R were also used to clean population data from the American Community Survey. Packages used include: Dplyr, data.table, and tidyverse to clean and organize data from the EPA and ACS. ArcGIS Pro was used to compute spatial data regarding sites in the risk zone and vulnerable populations. DEM data processed for each state removed any elevation data above 10m, keeping anything 10m and below. The Intersection tool was used to identify superfund sites within the 10m sea level rise risk zone. The Calculate Geometry tool was used to calculate the area within each coastal state that was occupied by the 10m SLR zone and used again to calculate the area of each superfund site. Summary Statistics were used to generate the total proportion of superfund site surface area / 10m SLR area for each state. To generate population estimates of socially vulnerable households in proximity to superfund sites, we followed methods similar to that of Carter and Kalman (2020). First, we generated buffers at the 1km, 3km, and 5km distance of superfund sites. Then, using Tabulate Intersection, the estimated population of each census block group within each buffer zone was calculated. Summary Statistics were used to generate total numbers for each state. Bay Area Data Processing: In this regional study, we compared the groundwater elevation projections by Befus et al (2020) to a combined dataset of contaminated sites that we built from two separate databases (Envirostor and GeoTracker) that are maintained by two independent agencies of the State of California (DTSC and WRCB). We used ArcGIS to manage both the groundwater surfaces, as raster files, from Befus et al (2020) and the State’s point datasets of street addresses for contaminated sites. We used SF BCDC (2020) as the source of social vulnerability rankings for census blocks, using block shapefiles from the US Census (ACS) dataset. In addition, we generated isolines that represent the magnitude of change in groundwater elevation in specific sea level rise scenarios. We compared these isolines of change in elevation to the USGS geological map of the San Francisco Bay region and noted that groundwater is predicted to rise farther inland where Holocene paleochannels meet artificial fill near the shoreline. We also used maps of historic baylands (altered by dikes and fill) from the San Francisco Estuary Institute (SFEI) to identify the number of contaminated sites over rising groundwater that are located on former mudflats and tidal marshes. The contaminated sites' data from the California State Water Resources Control Board (WRCB) and the Department of Toxic Substances (DTSC) was clipped to our study area of nine-bay area counties. The study area does not include the ocean shorelines or the north bay delta area because the water system dynamics differ in deltas. The data was cleaned of any duplicates within each dataset using the Find Identical and Delete Identical tools. Then duplicates between the two datasets were removed by running the intersect tool for the DTSC and WRCB point data. We chose this method over searching for duplicates by name because some sites change names when management is transferred from DTSC to WRCB. Lastly, the datasets were sorted into open and closed sites based on the DTSC and WRCB classifications which are shown in a table in the paper's supplemental material. To calculate areas of rising groundwater, we used data from the USGS paper “Projected groundwater head for coastal California using present-day and future sea-level rise scenarios” by Befus, K. M., Barnard, P., Hoover, D. J., & Erikson, L. (2020). We used the hydraulic conductivity of 1 condition (Kh1) to calculate areas of rising groundwater. We used the Raster Calculator to subtract the existing groundwater head from the groundwater head under a 1-meter of sea level rise scenario to find the areas where groundwater is rising. Using the Reclass Raster tool, we reclassified the data to give every cell with a value of 0.1016 meters (4”) or greater a value of 1. We chose 0.1016 because groundwater rise of that little can leach into pipes and infrastructure. We then used the Raster to Poly tool to generate polygons of areas of groundwater rise.

Description: This dataset consists of field data (arthropods, nematodes and NDVI) collected over the course of 6 field excursions in 2015 and 2016 near TyTy, GA, in a field used for growing Miscanthus x giganteus. It also includes interpolated values of soil measurements collected in 2015 and meteorological data collected on an adjacent farm. Point-in-time measurements include all meteorological, NDVI, arthropod and nematode measurements and their derivatives. Fixed values were measurements that were held constant across all sampling dates, including location, terrain and soils measurements and their derivatives. Dawn Olson and Jason Schmidt collected and processed arthropod count data. Jason Schmidt collected and processed spider count data and computed spider diversity. Richard Davis collected and processed nematode count data. Alisa Coffin collected and processed NDVI data and positional locations. Tim Strickland collected and processed soils data and Alisa Coffin interpolated soils values using kriging to derive values at arthropod sample locations. David Bosch collected and processed meteorological data. Lynne Seymour provided statistical expertise in deriving any estimated values (phloem feeders, parasitoids, spiders, and natural enemies). Alisa Coffin derived terrain data (elevation, slope, aspect, and distances) from publicly available datasets, transformed values (SI, WI, etc), carried out the geographically weighted regression analysis and calculated C:SE values, harmonized the full dataset, and compiled it using Esri's ArcGIS Pro 2.5. Methods for most data are published in the accompanying paper and associated supplements. Questions about dataset development and management should be directed to Alisa Coffin (alisa.coffin@usda.gov). This work was accomplished as a joint USDA and University of Georgia project funded by a cooperative agreement (#6048-13000-026-21S). This research was a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR is supported by the United States Department of Agriculture. At request of the author, the data resources are under embargo. The embargo will expire on Fri, Jan 01, 2021. Resources in this dataset:Resource Title: Spreadsheet of data. File Name: GibbsMisFarm_Arthrop_Env_DepVar_201516_final.xlsxResource Description: This workbook contains all of the data used in this analysis. The first worksheet contains data dictionary information.Resource Software Recommended: Microsoft Excel, Office 365,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: GeoJSON. File Name: MiscanthusXGiganteusGeoJSON.json

This dataset was updated May, 2025.This ownership dataset was generated primarily from CPAD data, which already tracks the majority of ownership information in California. CPAD is utilized without any snapping or clipping to FRA/SRA/LRA. CPAD has some important data gaps, so additional data sources are used to supplement the CPAD data. Currently this includes the most currently available data from BIA, DOD, and FWS. Additional sources may be added in subsequent versions. Decision rules were developed to identify priority layers in areas of overlap.Starting in 2022, the ownership dataset was compiled using a new methodology. Previous versions attempted to match federal ownership boundaries to the FRA footprint, and used a manual process for checking and tracking Federal ownership changes within the FRA, with CPAD ownership information only being used for SRA and LRA lands. The manual portion of that process was proving difficult to maintain, and the new method (described below) was developed in order to decrease the manual workload, and increase accountability by using an automated process by which any final ownership designation could be traced back to a specific dataset.The current process for compiling the data sources includes:* Clipping input datasets to the California boundary* Filtering the FWS data on the Primary Interest field to exclude lands that are managed by but not owned by FWS (ex: Leases, Easements, etc)* Supplementing the BIA Pacific Region Surface Trust lands data with the Western Region portion of the LAR dataset which extends into California.* Filtering the BIA data on the Trust Status field to exclude areas that represent mineral rights only.* Filtering the CPAD data on the Ownership Level field to exclude areas that are Privately owned (ex: HOAs)* In the case of overlap, sources were prioritized as follows: FWS > BIA > CPAD > DOD* As an exception to the above, DOD lands on FRA which overlapped with CPAD lands that were incorrectly coded as non-Federal were treated as an override, such that the DOD designation could win out over CPAD.In addition to this ownership dataset, a supplemental _source dataset is available which designates the source that was used to determine the ownership in this dataset. Data Sources:* GreenInfo Network's California Protected Areas Database (CPAD2023a). https://www.calands.org/cpad/; https://www.calands.org/wp-content/uploads/2023/06/CPAD-2023a-Database-Manual.pdf* US Fish and Wildlife Service FWSInterest dataset (updated December, 2023). https://gis-fws.opendata.arcgis.com/datasets/9c49bd03b8dc4b9188a8c84062792cff_0/explore* Department of Defense Military Bases dataset (updated September 2023) https://catalog.data.gov/dataset/military-bases* Bureau of Indian Affairs, Pacific Region, Surface Trust and Pacific Region Office (PRO) land boundaries data (2023) via John Mosley John.Mosley@bia.gov* Bureau of Indian Affairs, Land Area Representations (LAR) and BIA Regions datasets (updated Oct 2019) https://biamaps.doi.gov/bogs/datadownload.html Data Gaps & Changes:Known gaps include several BOR, ACE and Navy lands which were not included in CPAD nor the DOD MIRTA dataset. Our hope for future versions is to refine the process by pulling in additional data sources to fill in some of those data gaps. Additionally, any feedback received about missing or inaccurate data can be taken back to the appropriate source data where appropriate, so fixes can occur in the source data, instead of just in this dataset.25_1: The CPAD Input dataset was amended to merge large gaps in certain areas of the state known to be erroneous, such as Yosemite National Park, and to eliminate overlaps from the original input. The FWS input dataset was updated in February of 2025, and the DOD input dataset was updated in October of 2024. The BIA input dataset was the same as was used for the previous ownership version.24_1: Input datasets this year included numerous changes since the previous version, particularly the CPAD and DOD inputs. Of particular note was the re-addition of Camp Pendleton to the DOD input dataset, which is reflected in this version of the ownership dataset. We were unable to obtain an updated input for tribral data, so the previous inputs was used for this version.23_1: A few discrepancies were discovered between data changes that occurred in CPAD when compared with parcel data. These issues will be taken to CPAD for clarification for future updates, but for ownership23_1 it reflects the data as it was coded in CPAD at the time. In addition, there was a change in the DOD input data between last year and this year, with the removal of Camp Pendleton. An inquiry was sent for clarification on this change, but for ownership23_1 it reflects the data per the DOD input dataset.22_1 : represents an initial version of ownership with a new methodology which was developed under a short timeframe. A comparison with previous versions of ownership highlighted the some data gaps with the current version. Some of these known gaps include several BOR, ACE and Navy lands which were not included in CPAD nor the DOD MIRTA dataset. Our hope for future versions is to refine the process by pulling in additional data sources to fill in some of those data gaps. In addition, any topological errors (like overlaps or gaps) that exist in the input datasets may thus carry over to the ownership dataset. Ideally, any feedback received about missing or inaccurate data can be taken back to the relevant source data where appropriate, so fixes can occur in the source data, instead of just in this dataset.

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us online at https://goto.arcgisonline.com/earthobs3/Mean_Precipitation_WorldClimWorldClim 2.1 provides downscaled estimates of climate variables as monthly means over the period of 1970-2000 based on interpolated station measurements. Here we provide analytical image services of precipitation for each month along with an annual mean. Each time step is accessible from a processing template.Time Extent: Monthly/Annual 1970-2000Units: mm/monthCell Size: 2.5 minutes (~5 km)Source Type: StretchedPixel Type: 16 Bit IntegerData Projection: GCS WGS84Mosaic Projection: GCS WGS84Extent: GlobalSource: WorldClim v2.1Using Processing Templates to Access TimeThere are 13 processing templates applied to this service, each providing access to the 12 monthly and 1 annual mean precipitation layers. To apply these in ArcGIS Online, select the Image Display options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left-hand menu. From the Processing Template pull down menu, select the version to display.What can you do with this layer?This layer may be added to maps to visualize and quickly interrogate each pixel value. The pop-up provides a graph of the time series along with the calculated annual mean value.This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro and an area count of precipitation may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from month to month to show seasonal patterns.To calculate precipitation by land area, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Source Data: The datasets behind this layer were extracted from GeoTIF files produced by WorldClim at 2.5 minutes resolution. The mean of the 12 GeoTIFs was calculated (annual), and the 13 rasters were converted to Cloud Optimized GeoTIFF format and added to a mosaic dataset.Citation: Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.

The data here are large wood data collected in the Summer of 2022 in the Frying Pan River Basin in the Sawatch Mountains of Central Colorado. There are six datasets included or referenced here. The first is general geomorphic and watershed characteristics of the stream reaches surveyed. The second is data from field reports to the CAIC. The third is topographic data for the studied avalanche pathways. The fourth is summary data of the wood volumes within each surveyed reach. The fifth is the unprocessed raw data for all wood jams and individual pieces surveyed. The sixth is a table of literature-derived annual recruitment rates for mechanisms common to mountain streams. Data may also be accessed via the Dryad data repository as linked in the data accessibility statement. Raw data relate several wood jam and individual piece properties, including length, width, and depth of the former, and length and diameter of the latter. Data also indicate several other wood characteristics, such as piece orientation, stability and decay class, and the presence of a rootwad. Finally, data include information about the geomorphic impact of each surveyed piece and jam. Data were collected to examine research questions related to in-stream wood load volumes supplied by snow avalanches and the resultant geomorphic impacts. Methods Raw data were collected in the field by two trained observers. Wood loads were measured in the field using a census approach for all wood jams and individual wood pieces within the bankfull channel. Jams were identified as accumulations with three or more contiguous pieces; jam volume was quantified by measuring the length, width, and height of a rectangular prism fit to the dimensions of the jam and visually estimating porosity. Porosity was consistently estimated by two independent observers to minimize systematic bias. For individual wood pieces, diameter and length were measured and then used to compute volume via the formula of a cylinder. All measurements were made for wood at least partially contained within the bankfull channel, which was visually estimated in the field using topography (e.g., slope breaks). For wood pieces or jams that extended laterally beyond the bankfull dimensions, those portions outside were excluded from measurements. Measured wood loads were normalized by stream surface area (in ha) for comparisons between reaches with varied bankfull widths and lengths. These data have not been processed other than the above related volume calculations, which were then summarized across sites and watersheds (which have yielded the wood_volume_summary.csv data also presented here). Data regarding topographic and vegetation characteristics of the studied avalanche pathways were obtained via freely available remote datasets. These include a 1-m DEM for the study area available from USGS EarthExplorer (https://earthexplorer.usgs.gov/) and vegetation data from the LANDFIRE program (https://landfire.gov/). Canopy cover comes from the Existing Vegetation Cover raster in the LANDFIRE 2016 dataset. Planform curvature and slope angle were dervied from a 1-m DEM using the Curvature and Slope functions in ArcGIS Pro Version 2.8. Median was calculated for each pathway area using the Zonal Statistics function in ArcGIS Pro. Avalanche report data was gathered from Colorado Avalanche Information Center field reports (https://avalanche.state.co.us/observations/view-field-reports), restricting the "area" field to Sawatch and Aspen and the dates from 2019-03-01 to 2019-03-15. Literature data were gathered using a publication database/search engine (scholar.google.com) to find relevant sources via keyword sources. Data were then processed using the information in each publication to determine rates in units of m3/ha/yr. NAs in all datasets mean that the measure in question was inapplicable to the parameter under consideration. An open-source R code is provided to re-create data processing and figure creation.

Urban Tree Canopy Assessment. This was created using the Urban Tree Canopy Syracuse 2010 (All Layers) file HERE.The data for this map was created using LIDAR and other spatial analysis tools to identify and measure tree canopy in the landscape. This was a collaboration between the US Forest Service Northern Research Station (USFS), the University of Vermont Spatial Laboratory, and SUNY ESF. Because the full map is too large to be viewed in ArcGIS Online, this has been reduced to a vector tile layer to allow it to be viewed online. To download and view the shapefiles and all of the layers, you can download the data HERE and view this in either ArcGIS Pro or QGIS.Data DictionaryDescription source  USDA Forest ServiceList of values  Value 1 Description Tree CanopyValue 2 Description Grass/ShrubValue 3 Description Bare SoilValue 4 Description WaterValue 5 Description BuildingsValue 6 Description Roads/RailroadsValue 7 Description Other PavedField Class Alias Class Data type String Width 20Geometric objects  Feature class name landcover_2010_syracusecity Object type  complex Object count 7ArcGIS Feature Class Properties Feature class name landcover_2010_syracusecity Feature type  Simple Geometry type Polygon Has topology FALSE Feature count 7 Spatial index TRUE Linear referencing  FALSEDistributionAvailable format  Name ShapefileTransfer options  Transfer size 163.805Description Downloadable DataFieldsDetails for object landcover_2010_syracusecityType Feature Class Row count  7 Definition  UTCField FIDAlias FID Data type OID Width  4 Precision 0 Scale 0Field descriptionInternal feature number.Description source ESRIDescription of valueSequential unique whole numbers that are automatically generated.Field ShapeAlias Shape Data type Geometry Width 0 Precision 0 Scale 0Field description Feature geometry.Description source  ESRIDescription of values Coordinates defining the features.Field CodeAlias Code Data type Number Width 4Overview Description  Metadata DetailsMetadata language  English Metadata character set utf8 - 8 bit UCS Transfer FormatScope of the data described by the metadata  dataset Scope name  datasetLast update 2011-06-02ArcGIS metadata properties Metadata format ArcGIS 1.0 Metadata style North American Profile of ISO19115 2003Created in ArcGIS for the item 2011-06-02 16:48:35 Last modified in ArcGIS for the item 2011-06-02 16:44:43Automatic updates Have been performed Yes Last update 2011-06-02 16:44:43Item location history  Item copied or moved 2011-06-02 16:48:35 From T:\TestSites\NY\Syracuse\Temp\landcover_2010_syracusecity To \T7500\F$\Export\LandCover_2010_SyracuseCity\landcover_2010_syracusecity

The nine-banded Armadillo (Dasypus novemcinctus) is the only species of Armadillo in the United States and alters ecosystems by excavating extensive burrows used by many other wildlife species. Relatively little is known about its habitat use or population densities, particularly in developed areas, which may be key to facilitating its range expansion. We evaluated Armadillo occupancy and density in relation to anthropogenic and landcover variables in the Ozark Mountains of Arkansas along an urban to rural gradient. Armadillo detection probability was best predicted by temperature (positively) and precipitation (negatively). Contrary to expectations, occupancy probability of Armadillos was best predicted by slope (negatively) and elevation (positively) rather than any landcover or anthropogenic variables. Armadillo density varied considerably between sites (ranging from a mean of 4.88 – 46.20 Armadillos per km2) but was not associated with any environmental or anthropogenic variables. Methods Site Selection Our study took place in Northwest Arkansas, USA, in the greater Fayetteville metropolitan area. We deployed trail cameras (Spypoint Force Dark (Spypoint Inc, Victoriaville, Quebec, Canada) and Browning Strikeforce XD cameras (Browning, Morgan, Utah, USA) over the course of two winter seasons, December 2020-March 2021, and November 2021-March 2022. We sampled 10 study sites in year one, and 12 study sites in year two. All study sites were located in the Ozark Mountains ecoregion in Northwest Arkansas. Sites were all Oak Hickory dominated hardwood forests at similar elevation (213.6 – 541 m). Devils Eyebrow and ONSC are public natural areas managed by the Arkansas Natural heritage Commission (ANHC). Devil’s Den and Hobbs are managed by the Arkansas state park system. Markham Woods (Markham), Ninestone Land Trust (Ninestone) and Forbes, are all privately owned, though Markham has a publicly accessible trail system throughout the property. Lake Sequoyah, Mt. Sequoyah Woods, Kessler Mountain, Lake Fayetteville, and Millsaps Mountain are all city parks and managed by the city of Fayetteville. Lastly, both Weddington and White Rock are natural areas within Ozark National Forest and managed by the U.S. Forest Service. We sampled 5 sites in both years of the study including Devils Eyebrow, Markham Hill, Sequoyah Woods, Ozark Natural Science Center (ONSC), and Kessler Mountain. We chose our study sites to represent a gradient of human development, based primarily on Anthropogenic noise values (Buxton et al. 2017, Mennitt and Fristrup 2016). We chose open spaces that were large enough to accommodate camera trap research, as well as representing an array of anthropogenic noise values. Since anthropogenic noise is able to permeate into natural areas within the urban interface, introducing human disturbance that may not be detected by other layers such as impervious surface and housing unit density (Buxton et al. 2017), we used dB values for each site as an indicator of the level of urbanization. Camera Placement We sampled ten study sites in the first winter of the study. At each of the 10 study sites, we deployed anywhere between 5 and 15 cameras. Larger study areas received more cameras than smaller sites because all cameras were deployed a minimum of 150m between one another. We avoided placing cameras on roads, trails, and water sources to artificially bias wildlife detections. We also avoided placing cameras within 15m of trails to avoid detecting humans. At each of the 12 study areas we surveyed in the second winter season, we deployed 12 to 30 cameras. At each study site, we used ArcGIS Pro (Esri Inc, Redlands, CA) to delineate the trail systems and then created a 150m buffer on each side of the trail. We then created random points within these buffered areas to decide where to deploy cameras. Each random point had to occur within the buffered areas and be a minimum of 150m from the next nearest camera point, thus the number of cameras at each site varied based upon site size. We placed all cameras within 50m of the random points to ensure that cameras were deployed on safe topography and with a clear field of view, though cameras were not set in locations that would have increased animal detections (game trails, water sources, burrows etc.). Cameras were rotated between sites after 5 or 10 week intervals to allow us to maximize camera locations with a limited number of trail cameras available to us. Sites with more than 25 cameras were active for 5 consecutive weeks while sites with fewer than 25 cameras were active for 10 consecutive weeks. We placed all cameras on trees or tripods 50cm above ground and at least 15m from trails and roads. We set cameras to take a burst of three photos when triggered. We used Timelapse 2.0 software (Greenberg et al. 2019) to extract metadata (date and time) associated with all animal detections. We manually identified all species occurring in photographs and counted the number of individuals present. Because density estimation requires the calculation of detection rates (number of Armadillo detections divided by the total sampling period), we wanted to reduce double counting individuals. Therefore, we grouped photographs of Armadillos into “episodes” of 5 minutes in length to reduce double counting individuals that repeatedly triggered cameras (DeGregorio et al. 2021, Meek et al. 2014). A 5 min threshold is relatively conservative with evidence that even 1-minute episodes adequately reduces double counting (Meek et al. 2014). Landcover Covariates To evaluate occupancy and density of Armadillos based on environmental and anthropogenic variables, we used ArcGIS Pro to extract variables from 500m buffers placed around each camera (Table 2). This spatial scale has been shown to hold biological meaning for Armadillos and similarly sized species (DeGregorio et al. 2021, Fidino et al. 2016, Gallo et al. 2017, Magle et al. 2016). At each camera, we extracted elevation, slope, and aspect from the base ArcGIS Pro map. We extracted maximum housing unit density (HUD) using the SILVIS housing layer (Radeloff et al. 2018, Table 2). We extracted anthropogenic noise from the layer created by Mennitt and Fristrup (2016, Buxton et al. 2017, Table 2) and used the “L50” anthropogenic sound level estimate, which was calculated by taking the difference between predicted environmental noise and the calculated noise level. Therefore, we assume that higher levels of L50 sound corresponded to higher human presence and activity (i.e. voices, vehicles, and other sources of anthropogenic noise; Mennitt and Fristrup 2016). We derived the area of developed open landcover, forest area, and distance to forest edge from the 2019 National Land Cover Database (NLDC, Dewitz 2021, Table 2). Developed open landcover refers to open spaces with less than 20% impervious surface such as residential lawns, cemeteries, golf courses, and parks and has been shown to be important for medium-sized mammals (Gallo et al. 2017, Poessel et al. 2012). Forest area was calculated by combing all forest types within the NLCD layer (deciduous forest, mixed forest, coniferous forest), and summarizing the total area (km2) within the 500m buffer. Distance to forest edge was derived by creating a 30m buffer on each side of all forest boundaries and calculating the distance from each camera to the nearest forest edge. We calculated distance to water by combining the waterbody and flowline features in the National Hydrogeography Dataset (U.S. Geological Survey) for the state of Arkansas to capture both permanent and ephemeral water sources that may be important to wildlife. We measured the distance to water and distance to forest edge using the geoprocessing tool “near” in ArcGIS Pro which calculates the Euclidean distance between a point and the nearest feature. We extracted Average Daily Traffic (ADT) from the Arkansas Department of Transportation database (Arkansas GIS Office). The maximum value for ADT was calculated using the Summarize Within tool in ArcGIS Pro. We tested for correlation between all covariates using a Spearman correlation matrix and removed any variable with correlation greater than 0.6. Pairwise comparisons between distance to roads and HUD and between distance to forest edge and forest area were both correlated above 0.6; therefore, we dropped distance to roads and distance to forest edge from analyses as we predicted that HUD and forest area would have larger biological impacts on our focal species (Kretser et al. 2008). Occupancy Analysis In order to better understand habitat associations while accounting for imperfect detection of Armadillos, we used occupancy modeling (Mackenzie et al. 2002). We used a single-species, single-season occupancy model (Mackenzie et al. 2002) even though we had two years of survey data at 5 of the study sites. We chose to do this rather than using a multi-season dynamic occupancy model because most sites were not sampled during both years of the study. Even for sites that were sampled in both years, cameras were not placed in the same locations each year. We therefore combined all sampling into one single-season model and created unique site by year combinations as our sampling locations and we used year as a covariate for analysis to explore changes in occupancy associated with the year of study. For each sampling location, we created a detection history with 7 day sampling periods, allowing presence/absence data to be recorded at each site for each week of the study. This allowed for 16 survey periods between 01 December 2020, and 11 March 2021 and 22 survey periods between 01 November 2021 and 24 March 2022. We treated each camera as a unique survey site, resulting in a total of 352 sites. Because not all cameras were deployed at the same time and for the same length of time, we used a staggered entry approach. We used a multi-stage fitting approach in which we

The base flow recession time constant (tau) is a hydrologic index that characterizes the ability of a ground-water system to supply flow to a stream draining from that system. Tau and other correlated hydrologic indices have been used as explanatory variables to greatly improve the predictive power of low-flow regression equations. Tau can also be used as an indicator of streamflow dependence on groundwater inflow to the channel. Tau values were calculated for 10 streamgages in the Niobrara National Scenic River study area. The calculated tau values were then used to create a kriged map. Kriging is a geostatistical method that can be used to determine optimal weights for measurements at sampled locations (streamgages) for the estimation of values at unsampled locations (ungaged sites). The kriged tau map could be used (1) as the basis for identifying areas with different hydrologic responsiveness, with differing potential to demonstrate the effects of management changes and (2) in the development of regional low-flow regression equations. The Geostatistical Analyst tools in ArcGIS Pro version 2.5.2 (Environmental Systems Research Institute, 2012) were used to create the kriged tau map and perform cross validation to determine the root mean square error (RMSE) of the tau map.

This layer shows computer ownership and internet access by education. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of the population age 25+ who are high school graduates (includes equivalency) and have some college or associate's degree in households that have no computer. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B28006 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This layer shows housing costs as a percentage of household income. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey. This layer is symbolized to show the percent of renter households that spend 30.0% or more of their household income on gross rent (contract rent plus tenant-paid utilities). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25070, B25091 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The Viewshed analysis layer is used to identify visible areas. You specify the places you are interested in, either from a file or interactively, and the Viewshed service combines this with Esri-curated elevation data to create output polygons of visible areas. Some questions you can answer with the Viewshed task include:What areas can I see from this location? What areas can see me?Can I see the proposed wind farm?What areas can be seen from the proposed fire tower?The maximum number of input features is 1000.Viewshed has the following optional parameters:Maximum Distance: The maximum distance to calculate the viewshed.Maximum Distance Units: The units for the Maximum Distance parameter. The default is meters.DEM Resolution: The source elevation data; the default is 90m resolution SRTM. Other options include 30m, 24m, 10m, and Finest.Observer Height: The height above the surface of the observer. The default value of 1.75 meters is an average height of a person. If you are looking from an elevation location such as an observation tower or a tall building, use that height instead.Observer Height Units: The units for the Observer Height parameter. The default is meters.Surface Offset: The height above the surface of the object you are trying to see. The default value is 0. If you are trying to see buildings or wind turbines add their height here.Surface Offset Units: The units for the Surface Offset parameter. The default is meters.Generalize Viewshed Polygons: Determine if the viewshed polygons are to be generalized or not. The viewshed calculation is based upon a raster elevation model which creates a result with stair-stepped edges. To create a more pleasing appearance, and improve performance, the default behavior is to generalize the polygons. This generalization will not change the accuracy of the result for any location more than one half of the DEM's resolution.By default, this tool currently works worldwide between 60 degrees north and 56 degrees south based on the 3 arc-second (approximately 90 meter) resolution SRTM dataset. Depending upon the DEM resolution pick by the user, different data sources will be used by the tool. For 24m, tool will use global dataset WorldDEM4Ortho (excluding the counties of Azerbaijan, DR Congo and Ukraine) 0.8 arc-second (approximately 24 meter) from Airbus Defence and Space GmbH. For 30m, tool will use 1 arc-second resolution data in North America (Canada, United States, and Mexico) from the USGS National Elevation Dataset (NED), SRTM DEM-S dataset from Geoscience Australia in Australia and SRTM data between 60 degrees north and 56 degrees south in the remaining parts of the world (Africa, South America, most of Europe and continental Asia, the East Indies, New Zealand, and islands of the western Pacific). For 10m, tool will use 1/3 arc-second resolution data in the continental United States from USGS National Elevation Dataset (NED) and approximately 10 meter data covering Netherlands, Norway, Finland, Denmark, Austria, Spain, Japan Estonia, Latvia, Lithuania, Slovakia, Italy, Northern Ireland, Switzerland and Liechtenstein from various authoritative sources.To learn more, read the developer documentation for Viewshed or follow the Learn ArcGIS exercise called I Can See for Miles and Miles. To use this Geoprocessing service in ArcGIS Desktop 10.2.1 and higher, you can either connect to the Ready-to-Use Services, or create an ArcGIS Server connection. Connect to the Ready-to-Use Services by first signing in to your ArcGIS Online Organizational Account:Once you are signed in, the Ready-to-Use Services will appear in the Ready-to-Use Services folder or the Catalog window:If you would like to add a direct connection to the Elevation ArcGIS Server in ArcGIS for Desktop or ArcGIS Pro, use this URL to connect: https://elevation.arcgis.com/arcgis/services. You will also need to provide your account credentials. ArcGIS for Desktop:ArcGIS Pro:The ArcGIS help has additional information about how to do this:Learn how to make a ArcGIS Server Connection in ArcGIS Desktop. Learn more about using geoprocessing services in ArcGIS Desktop.This tool is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

This layer is a subset of Global Landcover 1992- 2020 Layer. This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.Land Cover ProcessingTo provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015.Source dataThe datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.phpCitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

This layer shows Households by Type. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show Average Household Size and the Total Households in a bi-variate map. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B11001, B25010, B25044, DP02, DP04Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

The summaries are derived from the Ecopia vector land cover dataset (a non-public resource) and not from the Ecopia raster land cover dataset available on this open data site.Percentages for each land cover type were calculated as compared to Ecopia’s total delineated area per summary polygon:sum_area_land_cover_type / sum_area_all_Ecopia_delineated_polygons_

This layer shows total population count by sex and age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of the population that are considered dependent (ages 65+ and <18). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B01001Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This layer shows median household income by race and by age of householder. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Calculating the total volume of water stored in a landscape can be challenging. In addition to lakes and reservoirs, water can be stored in soil, snowpack, or even inside plants and animals, and tracking the all these different mediums is not generally possible. However, calculating the change in storage is easy - just subtract the water output from the water input. Using the GLDAS layers we can do this calculation for every month from January 2000 to the present day. The precipitation layer tells us the input to each cell and runoff plus evapotranspiration is the output. When the input is higher than the output during a given month, it means water was stored. When output is higher than input, storage is being depleted. Generally the change in storage should be close to the change in soil moisture content plus the change in snowpack, but it will not match up exactly because of the other storage mediums discussed above.Dataset SummaryThe GLDAS Change in Storage layer is a time-enabled image service that shows net monthly change in storage from 2000 to the present, measured in millimeters of water. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-2.1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!Phenomenon Mapped: Change in Water StorageUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS for Desktop. It is useful for scientific modeling, but only at global scales.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.By applying the "Calculate Anomaly" raster function, it is possible to view these data in terms of deviation from the mean, instead of total change in storage. Mean change in storage for a given month is calculated over the entire period of record - 2000 to present.Time: This is a time-enabled layer. By default, it will show the first month from the map's time extent. Or, if time animation is disabled, a time range can be set using the layer's multidimensional settings. If you wish to calculate the average, sum, or min/max change in storage over the time extent, change the mosaic operator used to resolve overlapping pixels. In ArcGIS Online, you do this in the "Image Display Order" tab. In ArcGIS Pro, use the "Data" ribbon. In ArcMap, it is in the 'Mosaic' tab of the layer properties window. The minimum time extent is one month, and the maximum is 8 years. Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.

This document describes the methodology behind the Housing Affordability Dashboard (2025–2030). It includes details on the datasets used (U.S. Census Bureau ACS, HUD housing data, and local datasets), preprocessing steps such as data cleaning and normalization, and the geospatial techniques applied in ArcGIS Pro and ArcGIS Online. The methodology also explains affordability metrics, how they were calculated, and the workflow for integrating results into the dashboard. The goal is to ensure transparency, reproducibility, and clarity in how housing affordability patterns were derived and presented.