This dataset contains three different datasets: 1) vector data showing site sizes and locations from archaeological field surveys conducted in 2019 by the author, 2) raster datasets of social and environmental zones on Zanzibar and a suitability model of late colonial sites on Zanzibar, and 3) vector datasets of a digitized 1907 map of the island. Also contained is a QGIS project file.

MSZSI: Multi-Scale Zonal Statistics [AgriClimate] Inventory -------------------------------------------------------------------------------------- MSZSI is a data extraction tool for Google Earth Engine that aggregates time-series remote sensing information to multiple administrative levels using the FAO GAUL data layers. The code at the bottom of this page (metadata) can be pasted into the Google Earth Engine JavaScript code editor and ran at https://code.earthengine.google.com/. Please refer to the associated publication: Peter, B.G., Messina, J.P., Breeze, V., Fung, C.Y., Kapoor, A. and Fan, P., 2024. Perspectives on modifiable spatiotemporal unit problems in remote sensing of agriculture: evaluating rice production in Vietnam and tools for analysis. Frontiers in Remote Sensing, 5, p.1042624. https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2024.1042624 Input options: [1] Country of interest [2] Start and end year [3] Start and end month [4] Option to mask data to a specific land-use/land-cover type [5] Land-use/land-cover type code from CGLS LULC [6] Image collection for data aggregation [7] Desired band from the image collection [8] Statistics type for the zonal aggregations [9] Statistic to use for annual aggregation [10] Scaling options [11] Export folder and label suffix Output: Two CSVs containing zonal statistics for each of the FAO GAUL administrative level boundaries Output fields: system:index, 0-ADM0_CODE, 0-ADM0_NAME, 0-ADM1_CODE, 0-ADM1_NAME, 0-ADMN_CODE, 0-ADMN_NAME, 1-AREA_PERCENT_LULC, 1-AREA_SQM_LULC, 1-AREA_SQM_ZONE, 2-X_2001, 2-X_2002, 2-X_2003, ..., 2-X_2020, .geo PREPROCESSED DATA DOWNLOAD The datasets available for download contain zonal statistics at 2 administrative levels (FAO GAUL levels 1 and 2). Select countries from Southeast Asia and Sub-Saharan Africa (Cambodia, Indonesia, Lao PDR, Myanmar, Philippines, Thailand, Vietnam, Burundi, Kenya, Malawi, Mozambique, Rwanda, Tanzania, Uganda, Zambia, Zimbabwe) are included in the current version, with plans to extend the dataset to contain global metrics. Each zip file is described below and two example NDVI tables are available for preview. Key: [source, data, units, temporal range, aggregation, masking, zonal statistic, notes] Currently available: MSZSI-V2_V-NDVI-MEAN.tar: [NASA-MODIS, NDVI, index, 2001–2020, annual mean, agriculture, mean, n/a] MSZSI-V2_T-LST-DAY-MEAN.tar: [NASA-MODIS, LST Day, °C, 2001–2020, annual mean, agriculture, mean, n/a] MSZSI-V2_T-LST-NIGHT-MEAN.tar: [NASA-MODIS, LST Night, °C, 2001–2020, annual mean, agriculture, mean, n/a] MSZSI-V2_R-PRECIP-SUM.tar: [UCSB-CHG-CHIRPS, Precipitation, mm, 2001–2020, annual sum, agriculture, mean, n/a] MSZSI-V2_S-BDENS-MEAN.tar: [OpenLandMap, Bulk density, g/cm3, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-ORGC-MEAN.tar: [OpenLandMap, Organic carbon, g/kg, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-PH-MEAN.tar: [OpenLandMap, pH in H2O, pH, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-WATER-MEAN.tar: [OpenLandMap, Soil water, % at 33kPa, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-SAND-MEAN.tar: [OpenLandMap, Sand, %, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-SILT-MEAN.tar: [OpenLandMap, Silt, %, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-CLAY-MEAN.tar: [OpenLandMap, Clay, %, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_E-ELEV-MEAN.tar: [MERIT, [elevation, slope, flowacc, HAND], [m, degrees, km2, m], static, n/a, agriculture, mean, n/a] Coming soon MSZSI-V2_C-STAX-MEAN.tar: [OpenLandMap, Soil taxonomy, category, static, n/a, agriculture, area sum, n/a] MSZSI-V2_C-LULC-MEAN.tar: [CGLS-LC100-V3, LULC, category, 2015–2019, mode, none, area sum, n/a] Data sources: https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13Q1 https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A2 https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_BULKDENS-FINEEARTH_USDA-4A1H_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_ORGANIC-CARBON_USDA-6A1C_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_PH-H2O_USDA-4C1A2A_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_WATERCONTENT-33KPA_USDA-4B1C_M_v01 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_CLAY-WFRACTION_USDA-3A1A1A_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_SAND-WFRACTION_USDA-3A1A1A_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX_C_v01...

This dataset contains 14 parquet-format files with monthly data.

    File
    Indicator
    Unit


    aet.parquet
    Actual Evapotranspiration
    mm


    def.parquet
    Climate Water Deficit
    mm


    pdsi.parquet
    Palmer Drought Severity Index (PDSI)
    unitless


    pet.parquet
    Precipitation
    mm


    ppt.parquet
    Potential evapotranspiration
    mm


    q.parquet
    Runoff
    mm


    soil.parquet
    Soil Moisture
    mm


    srad.parquet
    Downward surface shortwave radiation
    W/m2


    swe.parquet
    Snow water equivalent
    mm


    tmax.parquet
    Maximun Temperature
    °C


    tmin.parquet
    Minimum Temperature
    °C


    vap.parquet
    Vapor pressure
    kPa


    vpd.parquet
    Vapor Pressure Deficit
    kpq


    ws.parquet
    Wind speed
    m/s

This dataset presents daily weather indicators for Brazilian municipalities computed with zonal statistics using the data from the BR-DWGD project (version 3.2.3), from 1961-01-01 to 2024-03-20.

File Indicator Unit

pr_3.2.3.parquet Precipitation mm

ETo_3.2.3.parquet Evapotranspiration mm

Tmax_3.2.3.parquet Maximum temperature °C

Tmin_3.2.3.parquet Minimum temperature °C

Rs_3.2.3.parquet Solar radiation MJm-2

u2_3.2.3.parquet Wind speed at 2 m height m/s

RH_3.2.3.parquet Relative humidity %

The methodology to compute the zonal statistics follows https://doi.org/10.1017/eds.2024.3 .

The National Insect and Disease Risk map identifies areas with risk of significant tree mortality due to insects and plant diseases. The layer identifies lands in three classes: areas with risk of tree mortality from insects and disease between 2013 and 2027, areas with lower tree mortality risk, and areas that were formerly at risk but are no longer at risk due to disturbance (human or natural) between 2012 and 2018. Areas with risk of tree mortality are defined as places where at least 25% of standing live basal area greater than one inch in diameter will die over a 15-year time frame (2013 to 2027) due to insects and diseases.The National Insect and Disease Risk map, produced by the US Forest Service FHAAST, is part of a nationwide strategic assessment of potential hazard for tree mortality due to major forest insects and diseases. Dataset Summary Phenomenon Mapped: Risk of tree mortality due to insects and diseaseUnits: MetersCell Size: 30 meters in Hawaii and 240 meters in Alaska and the Contiguous USSource Type: DiscretePixel Type: 2-bit unsigned integerData Coordinate System: NAD 1983 Albers (Contiguous US), WGS 1984 Albers (Alaska), Hawaii Albers (Hawaii)Mosaic Projection: North America Albers Equal Area ConicExtent: Alaska, Hawaii, and the Contiguous United States Source: National Insect Disease Risk MapPublication Date: 2018ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/This layer was created from the 2018 version of the National Insect Disease Risk Map.What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "insects and disease" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "insects and disease" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use raster functions to create your own custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. For example, Zonal Statistics as Table tool can be used to summarize risk of tree mortality across several watersheds, counties, or other areas that you may be interested in such as areas near homes.In ArcGIS Online you can change then layer's symbology in the image display control, set the layer's transparency, and control the visible scale range.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

This feature layer contains geographically summarized data for temperature and precipiation thresholds of observed historical climate and modeled projections from the 5th National Climate Assessment. The methodology for generating the summaries can be found at the Climate Resilience Information System. Layer contents:43 climate variablesObserved history by gridded climatology (Livneh and nClimGrid)Modeled history for 16 general circulation models (GCMs) using two downscaling methods (LOCA2 and STAR), plus ensemblesFuture projections across three scenarios (shared socioeconomic pathways or SSPs) for 16 GCMs using two downscaling methods (LOCA2 and STAR), plus ensemblesDecadal and annual summariesCensus TIGER/Line county boundaries, 2023 vintage (link)Known issues and limitationsThere are no SSP370 data for STAR; this affects all associated GCMs and ensembles.Using the DataDue to the size of the layer, performance is limited when making advanced queries. Decadal averages are served in the feature layer and can be used for visualization.Annual averages are served using related tables based on the Geographic Identifier. These layers can be analyzed or subsetted. Visualziation in the Map Viewer is only possible if the fields are joined to the features using the Geographic Identifier. Additional tools are provided in the CRIS Developers Hub to assist in subsetting and analyzing these data. Additional LayersThe CRIS Open Data Hub provides a variety of geographically summarized data, including much smaller and performant summaries of climate projections based on the blended ensembles used in the 5th National Climate Assessment.

Estimates of malaria cases unseeking care by region in PNG (2011–2019).

Population density in 1990 within the boundaries of the Narragansett Bay watershed, the Southwest Coastal Ponds watershed, and the Little Narragansett Bay watershed. The methods for analyzing population were developed by the US Environmental Protection Agency ORD Atlantic Coastal Environmental Sciences Division in collaboration with the Narragansett Bay Estuary Program and other partners. Population rasters were generated using the USGS dasymetric mapping tool (see http://geography.wr.usgs.gov/science/dasymetric/index.htm) which uses land use data to distribute population data more accurately than simply within a census mapping unit. The 1990 10m cell population density raster was produced using Rhode Island 1988 state land use data, Massachusetts 1985 state land use, Connecticut 1992 NLCD land use data, and U.S. Census data (1990). To generate a population estimate (number of persons) for any given area within the boundaries of this raster, use the Zonal Statistics as Table tool to sum the 10m cell density values within your zone dataset (e.g., watershed polygon layer). For more information, please reference the 2017 State of Narragansett Bay & Its Watershed Technical Report (nbep.org).

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.

The tree canopy data was derived from the ArcGIS Living Atlas USA NLCD Tree Canopy Cover raster layer, which represents the canopy cover percentage for each 30-meter size raster cell. The granular raster data was rolled up to the census tract level by averaging the values for all the raster cells within each census tract (Ave Percent Tree Canopy Coverage). This was done with the Zonal Statistics as Table tool, similarly to the Air Quality workflow you saw earlier. The average percent tree canopy values were then used to determine the square kilometer coverage of tree canopy within each census tract (Total Canopy Coverage in Sq Km).Traffic data was also acquired from ArcGIS Living Atlas. The USA Traffic Counts point data was rolled up to the census tract for the city of Los Angeles (with the Summarize Within tool).

Mule deer populations continue to decline across much of the western United States due to loss of habitat, starvation, and severe climate patterns, such as drought. In order to track the home range size and ecological preferences of mule deer, an important species for culture, economy, and ecosystems, the New Mexico Bureau of Land Management Taos Field Office captured mule deer, attached collars to them, and released them into Rio Grande del Norte National Monument. Collected from 2015-2017, each unique entry is one deer during one year, for a total of 23 entries. The point data was then intersected with vegetation data in the area, and the density of points was determined through Kernel Density Estimation (KDE). Reclassified BLM Vegetation Treatment data was used for zonal statistics on the KDE data and offered insights into mule deer response to treatments. This project was conducted as a joint project between the NMBLM TFO, Fort Collins USGS Science Center, and Kent State University’s Biogeography & Landscape Dynamics lab. This dataset includes all spatial data (CPG, DBF, XLSX, PRJ, SBN, SBX, SHP, and SHX) files for the comprehensive location fix shapefile, the convex hulls, the reclassified LANDFIRE EVT raster, the analysis area, the reclassified BLM Vegetation Treatment groups, the Kernel Density Estimation result, and the hill shade and state boundary data.

‡ Robust standard errors clustered at the municipality level are presented in parentheses.Significance level: *** p < 0.01, ** p < 0.05. All dependent variables have been normalized between 0 and 1 to facilitate interpretation of coefficients. Night lights data takes a value between 0 and 63 for each approximately (1-km) pixel. Pixel data was aggregated at the municipality level using the zonal statistics package in QGIS in each year.Heterogeneity by type of DTO.‡

† Robust standard errors clustered at the municipality level are presented in parentheses.Significance level: *** p < 0.01. All dependent variables have been normalized between 0 and 1 to facilitate interpretation of coefficients. Night lights data takes a value between 0 and 63 for each approximately (1-km) pixel. Pixel data was aggregated at the municipality level using the zonal statistics package in QGIS in each year. Population data is from the 2010 Mexican Census. DTO presence (number) is a dummy that equals to 1 if a DTO is active in a municipality-year (the sum of presence across DTOs in a municipality-year).Drug trafficking organizations and local economic activity.†

We used a stratified-random sampling approach to estimate the total abundance of Wedge-tailed Shearwater (Ardenna pacifica) nest sites across Kīlauea Point National Wildlife Refuge (KPNWR), Kauaʻi, during 1-7 July 2019. We first identified strata as unique geographic areas of the refuge to account for potential differences in nesting habitat and non-uniform nest site clustering. We then sub-divided strata where we expected high, low, minimal, or no nest site abundance. These distinctions were based on knowledge of shearwater nesting distribution gained while performing extensive ground-searching for tropicbirds across the entire refuge in April and May 2019. We delineated strata boundaries using recent satellite imagery in ArcGIS (version 10.7) and, based on direct observations in the field, refined in order to remove large contiguous areas lacking shearwater presence or nesting habitat. Planar area of each polygon was automatically calculated by ArcGIS. To calculate surface area in each stratum, we obtained a 1/3 arc-second (10-m resolution) digital elevation model from the National Elevation Dataset (USGS 2013). This elevation raster was projected to Universal Transverse Mercator projection (Zone 4 North, North American Datum 1983) and converted to a surface area raster using the raster and sp packages in R (Hijmans 2019; Pebesma & Bivand 2005). We calculated stratum-specific surface area by summing the surface area values of raster cells in each stratum using XToolsPro (version 18; Zonal Statistics tool) in ArcMap (version 10.7). Hijmans RJ. 2019. raster: Geographic Data Analysis and Modeling. R package version 3.0-7. https://CRAN.R-project.org/package=raster Pebesma EJ, Bivand RS. 2005. Classes and methods for spatial data in R. R News 5(2):9-13. U.S. Geological Survey. 2013. USGS NED n23w160 1/3 arc-second 2013 1 x 1 degree ArcGrid: U.S. Geological Survey. Accessed at https://www.sciencebase.gov/catalog/item/581d21dae4b08da350d53be2

pockmarks are defined as depressions on the seabed and are usually formed by fluid expulsions. recently discovered, pockmarks along the aquitaine slope within the french eez, were manually mapped although two semi-automated methods were tested without convincing results. in order to potentially highlight different groups and possibly discriminate the nature of the fluids involved in their formation and evolution, a morphological study was conducted, mainly based on multibeam data and in particular bathymetry from the marine expedition gazcogne1, 2013. bathymetry and seafloor backscatter data, covering more than 3200 km², were acquired with the kongsberg em302 ship-borne multibeam echosounder of the r/v le suroît at a speed of ~8 knots, operated at a frequency of 30 khz and calibrated with ©sippican shots. precision of seafloor backscatter amplitude is +/- 1 db. multibeam data, processed using caraibes (©ifremer), were gridded at 15x15 m and down to 10x10 m cells, for bathymetry and seafloor backscatter, respectively. the present table includes 11 morphological attributes extracted from a geographical information system project (mercator 44°n conserved latitude in wgs84 datum) and additional parameters related to seafloor backscatter amplitudes. pockmark occurrence with regards to the different morphological domains is derived from a morphological analysis manually performed and based on gazcogne1 and bobgeo2 bathymetric datasets.the pockmark area and its perimeter were calculated with the “calculate geometry” tool of arcmap 10.2 (©esri) (https://desktop.arcgis.com/en/arcmap/10.3/manage-data/tables/calculating-area-length-and-other-geometric-properties.htm). a first method to calculate pockmark internal depth developed by gafeira et al. was tested (gafeira j, long d, diaz-doce d (2012) semi-automated characterisation of seabed pockmarks in the central north sea. near surface geophysics 10 (4):303-315, doi:10.3997/1873-0604.2012018). this method is based on the “fill” function from the hydrology toolset in spatial analyst toolbox arcmap 10.2 (©esri), (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/fill.htm) which fills the closed depressions. the difference between filled bathymetry and initial bathymetry produces a raster grid only highlighting filled depressions. thus, only the maximum filling values which correspond to the internal depths at the apex of the pockmark were extracted. for the second method, the internal pockmark depth was calculated with the difference between minimum and maximum bathymetry within the pockmark.latitude and longitude of the pockmark centroid, minor and major axis lengths and major axis direction of the pockmarks were calculated inside each depression with the “zonal geometry as table” tool from spatial analyst toolbox in arcgis 10.2 (©esri) (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-statistics.htm). pockmark elongation was calculated as the ratio between the major and minor axis length.cell count is the number of cells used inside each pockmark to calculate statistics (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-geometry.htm). cell count and minimum, maximum and mean bathymetry, slope and seafloor backscatter values were calculated within each pockmark with “zonal statistics as table” tool from spatial analyst toolbox in arcgis 10.2 (©esri). slope was calculated from bathymetry with “slope” function from spatial analyst toolbox in arcgis 10.2 (©esri) and preserves its 15 m grid size (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/slope.htm). seafloor backscatter amplitudes (minimum, maximum and mean values) of the surrounding sediments were calculated within a 100 m buffer around the pockmark rim.

This dataset contains the Sediment Thickness Model for Andalusia (South of Spain), the GroundHog files and the consulting scripts linked to the paper "Thickness model of Andalusian's nearshore and coastal inland topography " under review in Journal of Marine Science and Engineering.

The following ZIP files can be found here:

===============
STMA.zip

A dataset of 108 ESRI ASCII grid files group by province (Almeria, Cadiz, Granada, Huelva and Malaga) and by physiographic zone (18). Each zone has six different files named as:

PZZ_V_T_S.asc

where,

P: It is the first letter of the province (e.g. A for Almeria, C for Cadiz…)

ZZ: number of physiographic zone from a minimum number of 2 to 6 in each province. Each province was divided by different overlapping rectangles following on the orientation of the coastline, the shape of the continental shelf, river intersections, capes, the main sediment type, and the level of influence from atmospheric and maritime weathering agents, primarily.

V: the model version

T: Type of sediment, Consolidated (C) and Unconsolidated (U)

S: Grain size, Fine (F), Sand (S) and Gravel (G)

e.g. the file A01_1_C_F.asc is the province of Almeria, zone 01, version 1, Consolidated sediment and Fine fraction.

All files have a projection file (EPSG 25830) with the same name but a *.prj extension and a auxiliary file (*.asc.aux.xml) with additional information about the projection used.

===============
Script.zip

Specifically, three Pyqgis scripts and three Model Qgis:

• 1_Add_STMA_by_province_and_zone.py: to add the STMA to QGIS grouping the layers by province and zones.
• 2_Point_value_STMA.py: to add the STMA value to a point sample layer.
• 3_Zonal_statistics_STMA.py: to calculate the zonal statistics of STMA in a polygon area.
• 1_Volume_STMA.model3: Calculate the volume of each material (six raster layer) in one zone.
• 2_Volume_STMA_Clip.model3: Calculate the volume of each material in a clip area using a polygon layer.
• 3_Zonal_statistics_STMA.model3: Zonal statistics of the STMA in a polygon layer. It is a model version of the last pyqgis script 3_Zonal_statistics_STMA.py.

The first two models could be used in a batch processing if the user needs information of more than one zone.

===============
GroundHog.zip

Five folders, one for each province, with information to open the models in Groundhog software.