This dataset contains shapefiles and associated metadata for Kilauea volcano's Puu Oo episode 61g lava flow from May 24, 2016 through May 31, 2017. Episode 61g began with a breakout from the east flank of Puu Oo on May 24, 2016. Lava reached the Pacific Ocean at Kamokuna on July 26, 2017, and began building a lava delta that extended seaward from the original coastline. This lava delta collapsed into the ocean on December 31, 2016, as reflected in the data for January 12, 2017 and thereafter. The episode 61g lava flow continues as of May 31, 2017, the date of the last mapping to contribute to this dataset. One mapping date is included for each calendar month - usually late in the month - from May 2016 through May 2017, with two exceptions: two mapping dates are included for June 2016 to demonstrate the early expansion of the lava flow, and no mapping data were available for April 2017, so data from May 3, 2017 are included instead. Two shapefiles are associated with each mapping date: a polyline shapefile for the lava flow contacts with their attributes, and a polygon shapefile for the full extent of the lava flow on that date. In total, this dataset contains 28 shapefiles with associated metadata for 14 separate mapping dates. The lava flow contacts were mapped on the ground using GPS or digitized from images collected by a variety of aerial and satellite sources; the metadata include detailed descriptions of these sources.

Flood Hatch ShapefilesIn addition to the three sets of rasters (Maximum Wave Heights, Water Surface Elevations, and DFEs) provided, separate shapefiles were also created to overlap and highlight special areas within the raster datasets produced for calculating DFEs. A flood hatch shapefile is not provided for every ACFEP level or for every region, but when it is provided, it encompasses the special areas for that level and region. The same hatch shapefile is applicable for all datatypes for the particular level and region. Flood hatch shapefiles encompass all areas of special values within the data rasters (including areas of 9999, 9998, and 9997 values). All regions have a 0.1% ACFEP level flood hatch shapefile because all 0.1% ACFEP rasters contain 9999 values.The flood hatch shapefiles contain individual polygons that describe the type of special area underlying that polygon’s spatial extent. For 9999 and 9998 values in the value rasters (water surface elevations, waves, and DFEs), the special hatched polygons will have the same extent of those values within those rasters. For 9997 values in the value rasters, the hatch polygon will always encompass the 9997 values, but may be larger in extent than just the location of those value cells. For these areas, water surface elevation, wave heights, and DFEs values may be provided, but they still represent a special zone.The Hatch polygons have 5 fields (Column headers) that describe each polygon within the shapefile. These fields include FID, Shape, Hatch_Type, Zones_txt, Hatch, and Hatch_Txt. The FID field contains an ID number for each polygon within that shapefile, while the Shape fieldlists the type of shapefile contained (polygon in all cases). The Hatch_Type field contains the numerical value that can be found within the value rasters (wave height, water surface, and DFE) underlying that polygon. Zones_txt and Hatch_txt are string type fields that contain descriptors of the polygon type, while the Hatch Field contains a numerical value for the type of hatching (1 for 0.1% Edge Zone, 2 for Wave Overtopping Zones, 3 for Dynamic Zone). The following table is an example of what a flood hatch file’s attribute table might look like.FIDShapeHatch_TypeZones_TxtHatchHatch_Txt0Polygon9999Shallow water flooding during extreme storms10.1% Edge Zone1Polygon9997Influenced by wave overtopping (incl. 9997 areas)2Wave Overtopping Zone2Polygon9998Dynamic Landform Areas3Dynamic ZoneSpecifically, the various hatch shapefiles can be defined as follows:• FID 0 Hatch Type – These represent areas of shallow water flooding during extreme storms. These are locations where flooding can only be expected during the most extreme events (> 1000-year return period) or where there are only minor flood depths (shallow flooding) during 1000-year return period AEP. These values only appear in 0.1% ACFEP level since they only occur at the very upper extent of extreme flooding. Water surface elevation values in these regions can be set to 0.1 foot above the site-specific land elevation to provide an estimate of the water surface elevation. Site-specific survey information may be needed to determine the land elevation. These hatch areas directly match areas with 9999 values within the rasters.• FID 1 Hatch Type – These represent wave overtopping zones. These hatch layers encompass the 9997 areas, but also include areas that have known values. Hatched areas of this type covering 9997 values would be expected to experience flooding caused by intermittent wave spray and overtopping only. Hatched areas of this type covering locations with values indicate that the flooding is caused by both direct sheet flow and wave overtopping. These hatched zones are provided for informational purposes by identifying zones that may require special design considerations for wave overtopping. Site-specific coastal processes analysis may also be required in these areas.• FID 2 Hatch Type – These represent areas where geomorphology is extremely dynamic and as such expected flooding may vary drastically. These values can appear in any ACFEP level. There are minimal locations of this type. These hatch areas directly match areas with 9998 values within the rasters.

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - No changes v1.0 - Initial public release Description Borders of all Water Management Areas (WMAs) across the 11 western-most states of the coterminous United States are available filtered through a single source. The legal name for this set of boundaries varies state-by-state. The data is provided as two compressed shapefiles. One, stateWMAs, contains data for all 11 states. For 10 of those states, Arizona being the exception, the polygons represent the legal management boundaries used by those states to manage their surface and groundwater resources respectively. WMAs refer to the set of boundaries a particular state uses to manage its water resources. Each set of boundaries was collected from the states individually, and then merged into one spatial layer. The merging process included renaming some columns to enable merging with all other source layers, as well as removing columns deemed not required for followup analysis. The retained columns for each boundary are: basinNum - the state provided unique numerical ID; basinName - the state provided English name of the area, where applicable; state - the state name; and uniID - a unique identifier we created by concatenating the state name, and underscore, and the state numerical ID. Arizona is unique within this collection of states in that surface and groundwater resources are managed using two separate sets of boundaries. During our followup analysis (Grogan et al., in review) we decided to focus on one set of boundaries, those for surface water. This is due to the recommendation of our hydrologists that the surface water boundary set is a more realist representation of how water moves across the landscape, as a few of the groundwater boundaries are based on political and/or economic considerations. Therefore, the Arizona surface WMAs are included within stateWMAs. The Arizona groundwater WMAs are provided as a separate file, azGroundWMAs, as a companion to the first file for completeness and general reference. WMA spatial boundary data sources by state: Arizona: Arizona Surface Water Watersheds; Collected February, 2020; https://gisdata2016-11-18t150447874z-azwater.opendata.arcgis.com/datasets/surface-watershed/explore?location=34.158174%2C-111.970823%2C7.50 Arizona: Arizona Ground Water Basins; Collected February, 2020; https://gisdata2016-11-18t150447874z-azwater.opendata.arcgis.com/datasets/groundwater-basin-2/explore?location=34.158174%2C-111.970823%2C7.50 California: California CalWater 2.2.1; Collected February, 2020; https://www.mlml.calstate.edu/mpsl-mlml/data-center/data-entry-tools/data-tools/gis-shapefile-layers/ Colorado: Colorado Water District Boundaries; Collected February, 2020; https://www.colorado.gov/pacific/cdss/gis-data-category Idaho: Idaho Department of Water Resources (IDWR) Administrative Basins; Collected November, 2015; https://data-idwr.opendata.arcgis.com/datasets/fb0df7d688a04074bad92ca8ef74cc26_4/explore?location=45.018686%2C-113.862284%2C6.93 Montana: Collected June, 2019; Directly contacted Montana Department of Natural Resources and Conservation (DNRC) Office of Information Technology (OIT) Nevada: Nevada State Engineer Admin Basin Boundaries; Collected April, 2020 https://ndwr.maps.arcgis.com/apps/mapviewer/index.html?layers=1364d0c3a0284fa1bcd90f952b2b9f1c New Mexico: New Mexico Office of the State Engineer (OSE) Declared Groundwater Basins; Collected April, 2020 https://geospatialdata-ose.opendata.arcgis.com/datasets/ose-declared-groundwater-basins/explore?location=34.179783%2C-105.996542%2C7.51 Oregon: Oregon Water Resources Department (OWRD) Administrative Basins; Collected February, 2020; https://www.oregon.gov/OWRD/access_Data/Pages/Data.aspx Utah: Utah Adjudication Books; Collected April, 2020; https://opendata.gis.utah.gov/datasets/utahDNR::utah-adjudication-books/explore?location=39.497165%2C-111.587782%2C-1.00 Washington: Washington Water Resource Inventory Areas (WRIA); Collected June, 2017; https://ecology.wa.gov/Research-Data/Data-resources/Geographic-Information-Systems-GIS/Data Wyoming: Wyoming State Engineer's Office Board of Control Water Districts; Collected June, 2019; Directly contacted Wyoming State Engineer's Office

Very little information is known about the distribution and abundance of snow petrels at the regional scale. This dataset contains locations of bird nests, mostly snow petrels, mapped in the Windmill Islands during the 2002-2003 season. Location of nests were recorded with handheld GPS receivers connected to a pocket PC and stored as a shapefile using Arcpad (ESRI software). Descriptive information relating to each bird nest was recorded and a detailed description of data fields is provided in the detailed description of the shapefiles.

Two observers conducted the surveys using distinct methodologies, Frederique Olivier (FO) and Drew Lee (DL). Three separate nest location files (ArcView point shapefiles) were produced and correspond to each of the survey methodologies used. Methodology 1 was the use of 200*200 m grid squares in which exhaustive searches were conducted (FO). Methodology 2 was the use of 2 transects within each the 200*200 m grid squares; methodology 3 was the use of 4 small quadrats (ca 25 m) located within the 200*200m grid squares (DL). Nests mapped in a non-systematic manner (not following a specific methodology) are clearly identified within each dataset. Datasets were kept separate due to the uncertainties caused by GPS errors (the same nest may have different locations due to GPS error).

Three separate shapefiles describe survey methodologies:
- one polygon shapefile locates the 200*200 grid sites searched systematically (FO)
- one polygon shapefile locates the small quadrats (DL)
- one line shapefile locates line transects (DL)

Spatial characteristics, date of survey, search effort, number of nests found and other parameters are recorded for the grid sites, transect and quadrats.

See the word document in the file download for more information.

This work has been completed as part of ASAC project 1219 (ASAC_1219).

The fields in this dataset are:

Species
Activity
Type
Entrances
Slope
Remnants
Latitude
Longitude
Date
Snow
Eggchick
Cavitysize
Cavitydepth
Distnn
Substrate
Comments
SitedotID
Aspect
Firstfred
Systematic/Edge/Incidental
RecordCode

The full dataset, including a word document providing further information about the dataset, is publicly available for download from the provided URL.

Also available for download from another URL is polygon data representing flying bird nesting areas. The polygon data was derived from the flying bird nest locations by the Australian Antarctic Data Centre for displaying on maps.

The Northern Circumpolar Soil Carbon Database version 2 (NCSCDv2) is a geospatial database created for the purpose of quantifying storage of organic carbon in soils of the northern circumpolar permafrost region down to a depth of 300 cm. The NCSCDv2 is based on polygons from different regional soils maps homogenized to the U.S. Soil Taxonomy. The NCSCDv2 contains information on fractions of coverage of different soil types (following U.S. Soil Taxonomy nomenclature) as well as estimated storage of soil organic carbon (kg/m2) between 0-30 cm, 0-100 cm, 100-200 cm and 200-300 cm depth. The database was compiled by combining and homogenizing several regional/national soil maps. To calculate storage of soil organic carbon, these soil maps have been linked to field-data on soil organic carbon storage from sites with circumpolar coverage.

More information on database processing and properties can be found in the product guide.

The data is stored as ESRI shapefiles with associated attribute table databases. There are separate zipped data-folders with: (1) a merged circumpolar dataset in the Lambert Azimuthal Equal Area (LAEA) projection, (2) a merged circumpolar dataset geographic latitude/longitude coordinates (WGS84), (3) all regions in separate shape-files, in LAEA projection and (4) all regions in separate shape-files with geographic latitude/longitude coordinates (WGS84).

Citation

In order to use these data, you must cite this data set with the following citation:

Hugelius G, Bockheim JG, Camill, P, Elberling B, Grosse G, Harden JW, Johnson K, Jorgenson T, Koven C, Kuhry P, Michaelson G, Mishra U, Palmtag J, Ping C-L, O’Donnell J, Schirrmeister L, Schuur EAG, Sheng Y, Smith LC, Strauss J, Yu Z. (2013) A new dataset for estimating organic carbon storage to 3m depth in soils of the northern circumpolar permafrost region. Earth System Science Data, 5, 393–402, doi:10.5194/essd-5-393-2013.

This data is experimental, see the ‘Access Constraints or User Limitations’ section for more details. This dataset has been generalised to 10 metre resolution where it is still but the space needed for downloads will be improved.A set of UK wide estimated travel area geometries (isochrones), from Output Area (across England, Scotland, and Wales) and Small Area (across Northern Ireland) population-weighted centroids. The modes used in the isochrone calculations are limited to public transport and walking. Generated using Open Trip Planner routing software in combination with Open Street Maps and open public transport schedule data (UK and Ireland).The geometries provide an estimate of reachable areas by public transport and on foot between 7:15am and 9:15am for a range of maximum travel durations (15, 30, 45 and 60 minutes). For England, Scotland and Wales, these estimates were generated using public transport schedule data for Tuesday 15th November 2022. For Northern Ireland, the date used is Tuesday 6th December 2022.The data is made available as a set of ESRI shape files, in .zip format. This corresponds to a total of 18 files; one for Northern Ireland, one for Wales, twelve for England (one per English region, where London, South East and North West have been split into two files each) and four for Scotland (one per NUTS2 region, where the ‘North-East’ and ‘Highlands and Islands’ have been combined into one shape file, and South West Scotland has been split into two files).The shape files contain the following attributes. For further details, see the ‘Access Constraints or User Limitations’ section:AttributeDescriptionOA21CD or SA2011 or OA11CDEngland and Wales: The 2021 Output Area code.Northern Ireland: The 2011 Small Area code.Scotland: The 2011 Output Area code.centre_latThe population-weighted centroid latitude.centre_lonThe population-weighted centroid longitude.node_latThe latitude of the nearest Open Street Map “highway” node to the population-weighted centroid.node_lonThe longitude of the nearest Open Street Map “highway” node to the population-weighted centroid.node_distThe distance, in meters, between the population-weighted centroid and the nearest Open Street Map “highway” node.stop_latThe latitude of the nearest public transport stop to the population-weighted centroid.stop_lonThe longitude of the nearest public transport stop to the population-weighted centroid.stop_distThe distance, in metres, between the population-weighted centroid and the nearest public transport stop.centre_inBinary value (0 or 1), where 1 signifies the population-weighted centroid lies within the Output Area/Small Area boundary. 0 indicates the population-weighted centroid lies outside the boundary.node_inBinary value (0 or 1), where 1 signifies the nearest Open Street Map “highway” node lies within the Output Area/Small Area boundary. 0 indicates the nearest Open Street Map node lies outside the boundary.stop_inBinary value (0 or 1), where 1 signifies the nearest public transport stop lies within the Output Area/Small Area boundary. 0 indicates the nearest transport stop lies outside the boundary.iso_cutoffThe maximum travel time, in seconds, to construct the reachable area/isochrone. Values are either 900, 1800, 2700, or 3600 which correspond to 15, 30, 45, and 60 minute limits respectively.iso_dateThe date for which the isochrones were estimated, in YYYY-MM-DD format.iso_typeThe start point from which the estimated isochrone was calculated. Valid values are:from_centroid: calculated using population weighted centroid.from_node: calculated using the nearest Open Street Map “highway” node.from_stop: calculated using the nearest public transport stop.no_trip_found: no isochrone was calculated.geometryThe isochrone geometry.iso_hectarThe area of the isochrone, in hectares.Access constraints or user limitations.These data are experimental and will potentially have a wider degree of uncertainty. They remain subject to testing of quality, volatility, and ability to meet user needs. The methodologies used to generate them are still subject to modification and further evaluation.These experimental data have been published with specific caveats outlined in this section. The data are shared with the analytical community with the purpose of benefitting from the community's scrutiny and in improving the quality and demand of potential future releases. There may be potential modification following user feedback on both its quality and suitability.For England and Wales, where possible, the latest census 2021 Output Area population weighted centroids were used as the starting point from which isochrones were calculated.For Northern Ireland, 2011 Small Area population weighted centroids were used as the starting point from which isochrones were calculated. Small Areas and Output Areas contain a similar number of households within their boundaries. 2011 data was used because this was the most up-to-date data available at the time of generating this dataset. Population weighted centroids for Northern Ireland were calculated internally but may be subject to change - in the future we aim to update these data to be consistent with Census 2021 across the UK.For Scotland, 2011 Output Area population-weighted centroids were used as the starting point from which isochrones were calculated. 2011 data was used because this was the most up-to-date data available at the time of work.The data for England, Scotland and Wales are released with the projection EPSG:27700 (British National Grid).The data for Northern Ireland are released with the projection EPSG:29902 (Irish Grid).The modes used in the isochrone calculations are limited to public transport and walking. Other modes were not considered when generating this data.A maximum value of 1.5 kilometres walking distance was used when generating isochrones. This approximately represents typical walking distances during a commute (based on Department for Transport/Labour Force Survey data and Travel Survey for Northern Ireland technical reports).When generating Northern Ireland data, public transport schedule data for both Northern Ireland and Republic of Ireland were used.Isochrone geometries and calculated areas are subject to public transport schedule data accuracy, Open Trip Planner routing methods and Open Street Map accuracy. The location of the population-weighted centroid can also influence the validity of the isochrones, when this falls on land which is not possible or is difficult to traverse (e.g., private land and very remote locations).The Northern Ireland public transport data were collated from several files, and as such required additional pre-processing. Location data are missing for two bus stops. Some services run by local public transport providers may also be missing. However, the missing data should have limited impact on the isochrone output. Due to the availability of Northern Ireland public transport data, the isochrones for Northern Ireland were calculated on a comparable but slight later date of 6th December 2022. Any potential future releases are likely to contained aligned dates between all four regions of the UK.In cases where isochrones are not calculable from the population-weighted centroid, or when the calculated isochrones are unrealistically small, the nearest Open Street Map ‘highway’ node is used as an alternative starting point. If this then fails to yield a result, the nearest public transport stop is used as the isochrone origin. If this also fails to yield a result, the geometry will be ‘None’ and the ‘iso_hectar’ will be set to zero. The following information shows a further breakdown of the isochrone types for the UK as a whole:from_centroid: 99.8844%from_node: 0.0332%from_stop: 0.0734%no_trip_found: 0.0090%The term ‘unrealistically small’ in the point above refers to outlier isochrones with a significantly smaller area when compared with both their neighbouring Output/Small Areas and the entire regional distribution. These reflect a very small fraction of circumstances whereby the isochrone extent was impacted by the centroid location and/or how Open Trip Planner handled them (e.g. remote location, private roads and/or no means of traversing the land). Analysis showed these outliers were consistently below 100 hectares for 60-minute isochrones. Therefore, In these cases, the isochrone point of origin was adjusted to the nearest node or stop, as outlined above.During the quality assurance checks, the extent of the isochrones was observed to be in good agreement with other routing software and within the limitations stated within this section. Additionally, the use of nearest node, nearest stop, and correction of ‘unrealistically small areas’ was implemented in a small fraction of cases only. This culminates in no data being available for 8 out of 239,768 Output/Small Areas.Data is only available in ESRI shape file format (.zip) at this release.https://www.openstreetmap.org/copyright

A collection of vector polygon features for water bodies within the Urban Development Boundary (UDB) and outside the UDB, approximately 938 square miles. The planimetric layer for Miami-Dade County was previously digitized in 2001 by Woolpert, in 2012 by GPI Geospatial (GPI), and in 2020 by GPI. GPIs 2022 update utilized a Geodatabase provided by ITD for Miami-Dade County containing two layers to be updated: Water and Edge of Pavement. GPI used the recent set of county orthoimages, produced by Woolpert in 2022, to perform the update of the various feature classes. The feature classes were clipped into smaller blocks. Compilers made adjustments or recollected any missing features. The corresponding attributes were assigned to each separate shapefile as adjustments and new collection occurred. The shapefiles were then merged back together into a full file. Topological checks were performed to make sure the linework did not cross unexpectedly, ends dangled, or strange gaps exist in the collected features. Depending on the layer, different checks were done using the existing data layer to evaluate the completeness of the overall collection. The final layers were quality checked for formatting and file corruption before being sent to the client for review. Personnel that collected this data are either photogrammetrists trained in stereo collection or editors trained in ortho-photography based collection. Items included in the feature class WATER BODIES (Polygons) are: Water bodies and water-under-the-bridge features Definition of particular fields in the Water feature class: Water = {0,1} where 0 = Feature does not represent water; 1 = Water feature. Type = {'', 'B'} where B = Water under the bridge. Where '' marks water locations without bridges. Possible combinations of these fields are: 0,''; 1,'';1, 'B'.Updated: Biennially The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

In late September 2017, intense precipitation associated with Hurricane Maria caused extensive landsliding across Puerto Rico. Much of the Las Marias municipality in central-western Puerto Rico was severely impacted by landslides., Landslide density in this region was mapped as greater than 25 landslides/km2 (Bessette-Kirton et al., 2019). In order to better understand the controlling variables of landslide occurrence and runout in this region, two 2.5-km2 study areas were selected and all landslides within were manually mapped in detail using remote-sensing data. Included in the data release are five separate shapefiles: geographic areas representing the mapping extent of the four distinct areas (map areas, filename: map_areas), initiation location polygons (source areas, filename: SourceArea), polygons of the entire impacted area consisting of source, transport, and deposition (affected areas, filename: AffectArea), points on the furthest upslope extent of the landslide source areas (headscarp point, filename: HSPoint), and lines reflecting the approximate travel paths from the furthest upslope extent to the furthest downslope extent of the landslides (runout lines, filename: RunoutLine). These shapefiles contain qualitative attributes interpreted from the aerial imagery (such as geomorphic setting and impact of human activity) and qualitative attributes extracted from the geospatial data (such as source area length, width, and depth), as well as attributes extracted from other sources (such as geology and soil properties). A table detailing each attribute, attribute abbreviations, the possible choices for each attribute, and a short description of each attribute is provided as a table in the file labeled AttributeDescription.docx. The headscarp point shapefile attribute tables contain closest distance between headscarp and paved road (road_d_m; road data from U.S. Census Bureau, 2015). The runout line shapefile attribute table reflects if the landslide was considered independently unmappable past a road or river (term_drain), the horizontal length of the runout (length_m), the fall height from the headscarp to termination (h_m), the ratio of fall height to runout length (hlratio), distance to nearest paved road (road_d_m), and the watershed area upslope from the upper end of the runout line (wtrshd_m2). All quantitative metrics were calculated using tools available in ESRI ArcMap v. 10.6. The source area shapefile attribute table reflects general source area vegetation (vegetat) and land use (land_use), whether the slide significantly disaggregated during movement (flow), the failure mode (failmode), if the slide was a reactivation of a previous one (reactivate), if the landslide directly impacted the occurrence of another slide (ls_complex), the proportion of source material that left the source area (sourc_evac), the state of the remaining material (remaining), the curvature of the source area (sourc_curv), potential human impact on landslide occurrence (human_caus), potential landslide impact on human society (human_effc), if a building exists within 10 meters of the source area (buildng10m), if a road exists within 50 meters of the source area (road50m), the planimetric area of the source area (area_m2), the dimension of the source area perpendicular to the direction of motion (width_m), the dimension of the source area parallel to the direction of motion (length_m), the geologic formation of the source area (FMATN; from Bawiec, W.J., 1998), the soil type of the source area (MUNAME; from Acevido, G., 2020), the root-zone (0-100 cm deep) soil moisture estimated by the NASA SMAP mission for 9:30 am Atlantic Standard Time on 21 September 2017 (the day after Hurricane MarÃa) (smap; NASA, 2017), the average precipitation amount in the source area for the duration of the hurricane (pptn_mm; from Ramos-Scharrón, C.E., and Arima, E., 2019), the source area mean slope (mn_slp_d), the source area median slope (mdn_slp_d), the average depth change of material from the source area after the landslide (mn_dpth_m), the median depth change of material from the source area after the landslide (mdn_dpt_m), the sum of the volumetric change of material in the source area after the landslide (ldr_sm_m3), the major geomorphic landform of the source (maj_ldfrm), and the landcover of the source area (PRGAP_CL; from Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan, 2004). The affected area shapefile attribute table reflects the general affected area vegetation type (vegetat), the major geomorphic landform on which the landslide occurred (maj_ldfrm), whether the slide disaggregated during movement (flow), the general land use (land_use), the planimetric area of the affected area (area_m2), the dominant geologic formation of the affected area (FMATN; from Bawiec, W.J., 1998), the dominant soil type of the affected area (MUNAME; from Acevido, G., 2020), the sum of the volumetric change of material in all the contributing source areas for the affected area (Sum_ldr_sm), the average volumetric change of material in all the contributing source areas for the affected area (Avg_ldr_sm), if the landslide was considered independently unmappable past a road or river (term_drain), the number of contributing source areas to the affected area (num_srce), and the dominant landcover of the affected area (PRGAP_CL; from Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan, 2004). Mapping was conducted using aerial imagery collected between 9-15 October 2017 at 25-cm resolution (Quantum Spatial, Inc., 2017), a 1-m-resolution pre-event lidar digital elevation model (DEM) (U.S. Geological Survey, 2018), and a 1-m-resolution post-event lidar DEM (U.S. Geological Survey, 2020). In order to accurately determine the extent of the mapped landslides and to verify the georeferencing of the aerial imagery, aerial photographs were overlain with each DEM as well as a pre- and post-event lidar difference (2016-2018), and corrections were made as needed. Additional data sources described in the AttributeDescription document and metadata were used to extract spatial data once mapping was complete and results were appended to the shapefile attribute tables. Data in this release are provided as ArcGIS point (HSPoint), line (RunoutLine), and polygon (AffectArea and SourceArea) feature class files. Bessette-Kirton, E.K., Cerovski-Darriau, C., Schulz, W.H., Coe, J.A., Kean, J.W., Godt, J.W, Thomas, M.A., and Hughes, K. Stephen, 2019, Landslides Triggered by Hurricane Maria: Assessment of an Extreme Event in Puerto Rico: GSA Today, v. 29, doi:10.1130/GSATG383A.1 U.S. Census Bureau, 2015, 2015 TIGER/Line Shapefiles, State, Puerto Rico, primary and secondary roads State-based Shapefile: United States Census Bureau, accessed September 12, 2019, at http://www2.census.gov/geo/tiger/TIGER2015/ PRISECROADS/tl_2015_72_prisecroads.zip. Bawiec, W.J., 1998, Geology, geochemistry, geophysics, mineral occurrences and mineral resource assessment for the Commonwealth of Puerto Rico: U.S. Geological Survey Open-File Report 98-38, https://pubs.usgs.gov/of/1998/of98-038/ (accessed May 2020). Acevido, G., 2020, Soil Survey of Arecibo Area of Norther Puerto Rico: United States Department of Agriculture, Soil Conservation Service. National Aeronautics and Space Administration [NASA], 2017, SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update, Version 4: National Snow & Ice Data Center web page, accessed September 12, 2019, at https://nsidc.org/data/SPL4SMAU/versions/4. Ramos-Scharrón, C.E., and Arima, E., 2019, Hurricane MarÃa’s precipitation signature in Puerto Rico—A conceivable presage of rains to come: Scientific Reports, v. 9, no. 1, article no. 15612, accessed February 28, 2020, at https://doi.org/10.1038/ s41598-019-52198-2. Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan, 2004, Development of a 2001 National Landcover Database for the United States: Photogrammetric Engineering and Remote Sensing, Vol. 70, No. 7, July 2004, pp. 829-840. Quantum Spatial, Inc., 2017, FEMA PR Imagery: https://s3.amazonaws.com/fema-cap-imagery/Others/Maria (accessed October 2017). U.S. Geological Survey, 2018, USGS NED Original Product Resolution PR Puerto Rico 2015: http://nationalmap.gov/elevation.html (accessed October 2018). U.S. Geological Survey, 2020, USGS NED Original Product Resolution PR Puerto Rico 2018: http://nationalmap.gov/elevation.html (accessed June 2020). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government

This dataset contains a collection of ESRI geodatabases that hold hazard and impact data derived as part of the Severe Wind Hazard Assessment for Western Australia (2017-2020) project.

There are separate geodatabases for each community examined in the project. Within each community, multiple TC scenarios were analysed for each community. The list of scenarios is included below.

Geodatabase structure

Within each geodatabase, the data is structured as set out below. The structure is repeated for each available scenario in that community. Note scenario id numbers have the hyphen ('-') removed in the

• Shapefiles |-- TCs within 50 km |-- Cat
• Cat

Scenarios

Scenairo Id number, TC intensity, Location

000-01322,3,Exmouth 013-00928,3,Exmouth 000-06481,5,Exmouth 003-03693,3,PortHedland 000-08534,5,PortHedland 012-06287,3,Broome 012-03435,5,Broome 006-00850,3,Karratha-Roebourne 009-07603,5,Karratha-Roebourne 011-01345,1,Carnarvon 003-05947,3,Carnarvon 011-02754,1,Geraldton 001-08611,3,Geraldton 007-05186,1,Perth bsh291978,1,Perth

World Countries provides a detailed basemap layer for the countries of the world. This layer has been designed to be used as a basemap and includes fields for official names and country codes, along with fields for continent and display. Particularly useful are the fields LAND_TYPE and LAND_RANK that separate polygons based on their size. These fields are helpful for rendering at different scales by providing the ability to turn off small islands that may clutter small-scale (zoomed out) views. The sources of this dataset are Esri, Garmin, U.S. Central Intelligence Agency (The World Factbook), and International Organization for Standardization (ISO). This layer was published in October 2024. It is updated every 12-18 months or as significant changes occur.

Context

I'm trying to make a Choropleth map over time of home sale prices by block in Brooklyn for the last 15 years to visualize gentrification. I have the entire dataset for all 5 boroughs of New York, but am starting with Brooklyn.

Content and Acknowledgements

Primary dataset is the NYC Housing Sales Data Found in this Link: http://www1.nyc.gov/site/finance/taxes/property-rolling-sales-data.page

The data in all the separate excel spreadsheets for 2003-2017 was merged via VBA scripting in Excel and further cleaned & de-duped in R

Additionally, in my hunt for shapefiles I discovered these wonderful shapefiles from NYCPluto: https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page

I left joined it by "Block" & "Lot" onto the primary data frame, but 25% of the block/lot combo's ended up not having a corresponding entry in the Pluto shapefile and are NAs.

Note that as in other uploaded datasets of NYC housing on Kaggle, many of these transactions have a sale_price of $0 or only a nominal amount far less than market value. These are likely property transfers to relatives and should be excluded from any analysis of market prices.

Inspiration

Can you model Brooklyn home prices accurately?

This file contains the map layers that were used to create the Eddy Arboretum - Web Map, which was then used to create the Eddy Arboretum Interactive Map. These files were created in ArcGIS Pro and then imported into ArcGIS Online. This file contains the files that were used to create a simulation of the original grid of the arboretum, including the minimum bounding geometry and the point_grid, which were created using ArcPy. The polygons were made on two separate grids for the east and northwest sections and then merged into one shapefile. Only the IFG_Arboretum_Lichen and the arboretum_merge are currently in the Web Map. arboretum_merge was renamed to Tree Species by Conservation Status.The grid was interpretted from the original 1937 map of the Eddy Arboretum, and was verified using a high-accuracy GPS. This is the first digitized version of this product. The lichen locations are less precise as they were GPSed with mobile satellite.You can find the full Eddy Arboretum Interactive Map (Web Experience) here. If you have questions or comments about the creation of this map or any specific details regarding the information within, please contact Kara Kaur Sanghera. Kara is now affiliated with the Department of Geography at the University of California, Los Angeles, and can be reached at karakaur01@ucla.edu. If you need to update the ArcGIS shapefiles found in this map, please contact Courtney Canning (courtney.a.canning@usda.gov) or Christopher Looney (christopher.looney@usda.gov).

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. The vegetation map consists of 267 polygons comprising an area of 1842.5 ha (4,552.8 ac) (Table 4). Average polygon size is 6.7 ha (16.6 ac). One-hundred-nineteen polygons were 100% dominated by one physiognomic vegetation class. The remaining polygons contain two physiognomic vegetation classes, of these: (1) 59 were 90% dominated by the primary physiognomic class; (2) 34 were 80% dominated by the primary class type; (3) 16 were 70% dominated by the primary class type; (4) 15 polygons were 60% dominated by the primary class type; and (5) 24 polygons had a 50/50 split between the two physiognomic vegetation class types present. Maps are produced in Universal Transverse Mercator (UTM) coordinates (NAD 83) with a 1:24,000 scale and a minimum mapping unit of 0.5 hectares (ha) (1.24 acres (ac)). This vegetation feature class was updated in 2017 to reflect the final Oregon Caves National Monument and Preserve boundary approved by the U.S. Congress on December 19, 2014, which deviated slightly from the original proposed expansion boundary and project area used in the original vegetation mapping effort.

The Watershed and Subwatershed hydrologic unit boundaries provide a uniquely identified and uniform method of subdividing large drainage areas. The smaller sized 6th level sub-watersheds (up to 250,000 acres) are useful for numerous application programs supported by a variety of local, State, and Federal Agencies. This data set is intended to be used as a tool for water-resource management and planning activities, particularly for site-specific and localized studies requiring a level of detail provided by large-scale map information. The dataset will be appended to a larger seamless nationally consistant geospatial database as other states complete their portion of the watershed boundary dataset. Two separate shapefiles were created for downloading purposes. One with arcs (wy_hu12arc.shp) and one with polygons (wy_hu12poly.shp). The same metadata is used for both shapefiles. Only the arc attributes will be found in the wy_hu12arc shapefile. Similarly, only the poly attributes will be found in the wy_hu12poly shapefile.

pcgpraqM is one of several Pima County Government Property Rights (PCGPR) layers. pcgpraqM displays Pima County Acquired Property Rights data, which include property, various easements, and other miscellaneous conveyances. The areas in question were either acquired by recorded deed or lease. Polygon features and attributes are based on recorded instruments. A nightly batch process appends all section shapefiles to create pcgpracq. The shape pcgpracq is further processed to join all area (polygon) values to related sql table attributes. If a duplicate/triplicate exists the polygon makes a copy of itself.The maintenance of this layer is handled by Pima County. For more detailed information, please refer to the original metadata, found here. PurposeShows information about property rights in Pima County.Dataset ClassificationLevel 0 - OpenKnown UsesUsed in the HP Dashboard Map.Known ErrorsThis layer has overlapping polygons that are not represented in the coverage format. Do not use a coverage format version of this layer. This layer is built from acquisition section drawings and related information stored in sql tables. This layer represents all document and/or classcode records related to an acquisition area (polygon). If an area (polygon) references more than one document and/or classcode, it creates a duplicate of itself and references the additional data. In that way all data is represented in the shapefile. In addition, the areas (polygons) are dissolved on common document data information. If a right of way area (polygon) was split during initial data entry along a section line, it is no longer represented by multiple polygons; the pieces are dissolved into one common area (polygon). As a result of the dissolve any BB_NO (unique identifier of the polygon disappears). In some cases, Pima County owned road Rights-Of-Way (ROW) do not encompass the entire portion of the overall road ROW.Data ContactPima County Information Technology Department - Geographic Information Systems201 N Stone Ave., 9th FloorTucson, AZ 85701GISDdata@pima.govUpdate FrequencyThe update of this layer is handled by Pima County. The last known update was 2014 but that date may not be accurate.

Flood Hatch ShapefilesIn addition to the three sets of rasters (Maximum Wave Heights, Water Surface Elevations, and DFEs) provided, separate shapefiles were also created to overlap and highlight special areas within the raster datasets produced for calculating DFEs. A flood hatch shapefile is not provided for every ACFEP level or for every region, but when it is provided, it encompasses the special areas for that level and region. The same hatch shapefile is applicable for all datatypes for the particular level and region. Flood hatch shapefiles encompass all areas of special values within the data rasters (including areas of 9999, 9998, and 9997 values). All regions have a 0.1% ACFEP level flood hatch shapefile because all 0.1% ACFEP rasters contain 9999 values.The flood hatch shapefiles contain individual polygons that describe the type of special area underlying that polygon’s spatial extent. For 9999 and 9998 values in the value rasters (water surface elevations, waves, and DFEs), the special hatched polygons will have the same extent of those values within those rasters. For 9997 values in the value rasters, the hatch polygon will always encompass the 9997 values, but may be larger in extent than just the location of those value cells. For these areas, water surface elevation, wave heights, and DFEs values may be provided, but they still represent a special zone.The Hatch polygons have 5 fields (Column headers) that describe each polygon within the shapefile. These fields include FID, Shape, Hatch_Type, Zones_txt, Hatch, and Hatch_Txt. The FID field contains an ID number for each polygon within that shapefile, while the Shape fieldlists the type of shapefile contained (polygon in all cases). The Hatch_Type field contains the numerical value that can be found within the value rasters (wave height, water surface, and DFE) underlying that polygon. Zones_txt and Hatch_txt are string type fields that contain descriptors of the polygon type, while the Hatch Field contains a numerical value for the type of hatching (1 for 0.1% Edge Zone, 2 for Wave Overtopping Zones, 3 for Dynamic Zone). The following table is an example of what a flood hatch file’s attribute table might look like.FIDShapeHatch_TypeZones_TxtHatchHatch_Txt0Polygon9999Shallow water flooding during extreme storms10.1% Edge Zone1Polygon9997Influenced by wave overtopping (incl. 9997 areas)2Wave Overtopping Zone2Polygon9998Dynamic Landform Areas3Dynamic ZoneSpecifically, the various hatch shapefiles can be defined as follows:• FID 0 Hatch Type – These represent areas of shallow water flooding during extreme storms. These are locations where flooding can only be expected during the most extreme events (> 1000-year return period) or where there are only minor flood depths (shallow flooding) during 1000-year return period AEP. These values only appear in 0.1% ACFEP level since they only occur at the very upper extent of extreme flooding. Water surface elevation values in these regions can be set to 0.1 foot above the site-specific land elevation to provide an estimate of the water surface elevation. Site-specific survey information may be needed to determine the land elevation. These hatch areas directly match areas with 9999 values within the rasters.• FID 1 Hatch Type – These represent wave overtopping zones. These hatch layers encompass the 9997 areas, but also include areas that have known values. Hatched areas of this type covering 9997 values would be expected to experience flooding caused by intermittent wave spray and overtopping only. Hatched areas of this type covering locations with values indicate that the flooding is caused by both direct sheet flow and wave overtopping. These hatched zones are provided for informational purposes by identifying zones that may require special design considerations for wave overtopping. Site-specific coastal processes analysis may also be required in these areas.• FID 2 Hatch Type – These represent areas where geomorphology is extremely dynamic and as such expected flooding may vary drastically. These values can appear in any ACFEP level. There are minimal locations of this type. These hatch areas directly match areas with 9998 values within the rasters.

This data is experimental, see the ‘Access Constraints or User Limitations’ section for more details. This dataset has been generalised to 10 metre resolution where it is still but the space needed for downloads will be improved.A set of UK wide estimated travel area geometries (isochrones), from Output Area (across England, Scotland, and Wales) and Small Area (across Northern Ireland) population-weighted centroids. The modes used in the isochrone calculations are limited to public transport and walking. Generated using Open Trip Planner routing software in combination with Open Street Maps and open public transport schedule data (UK and Ireland).The geometries provide an estimate of reachable areas by public transport and on foot between 7:15am and 9:15am for a range of maximum travel durations (15, 30, 45 and 60 minutes). For England, Scotland and Wales, these estimates were generated using public transport schedule data for Tuesday 15th November 2022. For Northern Ireland, the date used is Tuesday 6th December 2022.The data is made available as a set of ESRI shape files, in .zip format. This corresponds to a total of 18 files; one for Northern Ireland, one for Wales, twelve for England (one per English region, where London, South East and North West have been split into two files each) and four for Scotland (one per NUTS2 region, where the ‘North-East’ and ‘Highlands and Islands’ have been combined into one shape file, and South West Scotland has been split into two files).The shape files contain the following attributes. For further details, see the ‘Access Constraints or User Limitations’ section:AttributeDescriptionOA21CD or SA2011 or OA11CDEngland and Wales: The 2021 Output Area code.Northern Ireland: The 2011 Small Area code.Scotland: The 2011 Output Area code.centre_latThe population-weighted centroid latitude.centre_lonThe population-weighted centroid longitude.node_latThe latitude of the nearest Open Street Map “highway” node to the population-weighted centroid.node_lonThe longitude of the nearest Open Street Map “highway” node to the population-weighted centroid.node_distThe distance, in meters, between the population-weighted centroid and the nearest Open Street Map “highway” node.stop_latThe latitude of the nearest public transport stop to the population-weighted centroid.stop_lonThe longitude of the nearest public transport stop to the population-weighted centroid.stop_distThe distance, in metres, between the population-weighted centroid and the nearest public transport stop.centre_inBinary value (0 or 1), where 1 signifies the population-weighted centroid lies within the Output Area/Small Area boundary. 0 indicates the population-weighted centroid lies outside the boundary.node_inBinary value (0 or 1), where 1 signifies the nearest Open Street Map “highway” node lies within the Output Area/Small Area boundary. 0 indicates the nearest Open Street Map node lies outside the boundary.stop_inBinary value (0 or 1), where 1 signifies the nearest public transport stop lies within the Output Area/Small Area boundary. 0 indicates the nearest transport stop lies outside the boundary.iso_cutoffThe maximum travel time, in seconds, to construct the reachable area/isochrone. Values are either 900, 1800, 2700, or 3600 which correspond to 15, 30, 45, and 60 minute limits respectively.iso_dateThe date for which the isochrones were estimated, in YYYY-MM-DD format.iso_typeThe start point from which the estimated isochrone was calculated. Valid values are:from_centroid: calculated using population weighted centroid.from_node: calculated using the nearest Open Street Map “highway” node.from_stop: calculated using the nearest public transport stop.no_trip_found: no isochrone was calculated.geometryThe isochrone geometry.iso_hectarThe area of the isochrone, in hectares.Access constraints or user limitations.These data are experimental and will potentially have a wider degree of uncertainty. They remain subject to testing of quality, volatility, and ability to meet user needs. The methodologies used to generate them are still subject to modification and further evaluation.These experimental data have been published with specific caveats outlined in this section. The data are shared with the analytical community with the purpose of benefitting from the community's scrutiny and in improving the quality and demand of potential future releases. There may be potential modification following user feedback on both its quality and suitability.For England and Wales, where possible, the latest census 2021 Output Area population weighted centroids were used as the starting point from which isochrones were calculated.For Northern Ireland, 2011 Small Area population weighted centroids were used as the starting point from which isochrones were calculated. Small Areas and Output Areas contain a similar number of households within their boundaries. 2011 data was used because this was the most up-to-date data available at the time of generating this dataset. Population weighted centroids for Northern Ireland were calculated internally but may be subject to change - in the future we aim to update these data to be consistent with Census 2021 across the UK.For Scotland, 2011 Output Area population-weighted centroids were used as the starting point from which isochrones were calculated. 2011 data was used because this was the most up-to-date data available at the time of work.The data for England, Scotland and Wales are released with the projection EPSG:27700 (British National Grid).The data for Northern Ireland are released with the projection EPSG:29902 (Irish Grid).The modes used in the isochrone calculations are limited to public transport and walking. Other modes were not considered when generating this data.A maximum value of 1.5 kilometres walking distance was used when generating isochrones. This approximately represents typical walking distances during a commute (based on Department for Transport/Labour Force Survey data and Travel Survey for Northern Ireland technical reports).When generating Northern Ireland data, public transport schedule data for both Northern Ireland and Republic of Ireland were used.Isochrone geometries and calculated areas are subject to public transport schedule data accuracy, Open Trip Planner routing methods and Open Street Map accuracy. The location of the population-weighted centroid can also influence the validity of the isochrones, when this falls on land which is not possible or is difficult to traverse (e.g., private land and very remote locations).The Northern Ireland public transport data were collated from several files, and as such required additional pre-processing. Location data are missing for two bus stops. Some services run by local public transport providers may also be missing. However, the missing data should have limited impact on the isochrone output. Due to the availability of Northern Ireland public transport data, the isochrones for Northern Ireland were calculated on a comparable but slight later date of 6th December 2022. Any potential future releases are likely to contained aligned dates between all four regions of the UK.In cases where isochrones are not calculable from the population-weighted centroid, or when the calculated isochrones are unrealistically small, the nearest Open Street Map ‘highway’ node is used as an alternative starting point. If this then fails to yield a result, the nearest public transport stop is used as the isochrone origin. If this also fails to yield a result, the geometry will be ‘None’ and the ‘iso_hectar’ will be set to zero. The following information shows a further breakdown of the isochrone types for the UK as a whole:from_centroid: 99.8844%from_node: 0.0332%from_stop: 0.0734%no_trip_found: 0.0090%The term ‘unrealistically small’ in the point above refers to outlier isochrones with a significantly smaller area when compared with both their neighbouring Output/Small Areas and the entire regional distribution. These reflect a very small fraction of circumstances whereby the isochrone extent was impacted by the centroid location and/or how Open Trip Planner handled them (e.g. remote location, private roads and/or no means of traversing the land). Analysis showed these outliers were consistently below 100 hectares for 60-minute isochrones. Therefore, In these cases, the isochrone point of origin was adjusted to the nearest node or stop, as outlined above.During the quality assurance checks, the extent of the isochrones was observed to be in good agreement with other routing software and within the limitations stated within this section. Additionally, the use of nearest node, nearest stop, and correction of ‘unrealistically small areas’ was implemented in a small fraction of cases only. This culminates in no data being available for 8 out of 239,768 Output/Small Areas.Data is only available in ESRI shape file format (.zip) at this release.https://www.openstreetmap.org/copyright

This data set is a complete digital hydrologic unit boundary layer to the Subwatershed (12-digit) 6th level for the State of Wyoming.This data set consists of geo-referenced digital data and associated attributes created in accordance with the "Federal Standards For Delineation of Hydrologic Unit Boundaries 12/06/01" (http://www.ftw.nrcs.usda.gov/huc_data.html). The data set was developed by digitizing watershed boundary lines using 1:24,000 Enhanced Digital Raster Graphics (DRG-E) geo-referenced topographic image base maps and 1:100,000-scale draft boundary lines. The National Elevation Dataset (NED) was used to produce the 1:100,000-scale preliminary draft boundaries. Polygons are attributed with hydrologic unit codes for 4th level sub-basins, 5th level watersheds, 6th level subwatersheds, name, size, downsteam hydrologic unit, type of watershed, non-contributing areas and flow modification. Arcs are attributed with the highest hydrologic unit code for each watershed, linesource and a metadata reference file. Two separate shapefiles were created for downloading purposes. One with arcs (wy_hu12arc.shp) and one with polygons (wy_hu12poly.shp). The same metadata is used for both shapefiles. Only the arc attributes will be found in the wy_hu12arc shapefile. Similarly, only the poly attributes will be found in the wy_hu12poly shapefile.

The 1997 Monterey County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). This survey is unique in that three site visits were completed in the area. Each of the three site visits was digitized as a separate survey, the Salinas Valley in the spring, the entire county in the summer, and the Salinas Valley in the fall. The data was gathered using aerial photography and extensive field visits, the land use boundaries and attributes were digitized, and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s San Joaquin District. Quality control procedures were performed jointly by staff at DWR’s DPLA headquarters and San Joaquin District. The finalized data includes one countywide shapefile, two Salinas Valley shapefiles (land use vector data) and JPEG files (raster data from aerial imagery). Important Points about Using this Data Set: 1. The land use boundaries were either drawn on-screen using developed photoquads, or hand drawn directly on USGS quad maps and then digitized. They were drawn to depict observable areas of the same land use. They were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. 2. For the Salinas Valley portion of Monterey County, the survey was not a "snapshot" in time, but incorporated three field visits for agricultural areas. The land use attributes of each delineated area (polygon) were based upon the surveyor’s observations in the field at those times. For the DWG and shapefiles, the attributes in the files are the observations, not the interpreted results. 3. For the area of Monterey County outside of the Salinas Valley, the survey was a "snapshot" in time (summer). The indicated land use attributes of each delineated area (polygon) were based upon what the surveyor saw in the field at that time, and, to an extent possible, whatever additional information the aerial photography might provide. For example, the surveyor might have seen a cropped field in the photograph, and the field visit showed a field of corn, so the field was given a corn attribute. In another field, the photograph might have shown a crop that was golden in color (indicating grain prior to harvest), and the field visit showed newly planted corn. This field would be given an attribute showing a double crop, grain followed by corn. The DWR land use attribute structure allows for up to three crops per delineated area (polygon). In the cases where there were crops grown before the survey took place, the surveyor may or may not have been able to detect them from the field or the photographs. For crops planted after the survey date, the surveyor could not account for these crops. Thus, although the data is very accurate for that point in time, it may not be an accurate determination of what was grown in the fields for the whole year. If the area being surveyed does have double or multicropping systems, it is likely that there are more crops grown than could be surveyed with a "snapshot". 3. If the data is to be brought into a GIS for analysis of cropped (or planted) acreage, two things must be understood: a. The acreage of each field delineated is the gross area of the field. The amount of actual planted and irrigated acreage will always be less than the gross acreage, because of ditches, farm roads, other roads, farmsteads, etc. Thus, a delineated corn field may have a GIS calculated acreage of 40 acres but will have a smaller cropped (or net) acreage, maybe 38 acres. b. Double and multicropping must be taken into account. A delineated field of 40 acres might have been cropped first with grain, then with corn, and coded as such. To estimate actual cropped acres, the two crops are added together (38 acres of grain and 38 acres of corn) which results in a total of 76 acres of net crop (or planted) acres. 4. Water source and irrigation method information were not collected for this survey. 5. Not all land use codes will be represented in the survey.