Context

This dataset is part of a series that contains three other datasets:

• UN SDG - EU 27 sample datamart (subset for 7 KPIs)
• Selected Indicators from World Bank 2000-2019: 33 indicators from World Bank website for a data project
• EU 27 Energy sources - consumption 1990-2018 / energy production, import, export by source - consumption by sector

Content

Dataset components

Sources

Processing done

Source: A

Converted .shp files (same name as the one under "dataset components") into CSV by using GDAL 3.0.4, released 2020/01/28, offline, under Windows 10, using the following command line: ogr2ogr -f CSV

Source: B

The data from this source for the time being are not uploaded due to errors in processing the sources (i.e. formatting errors in both .shp files and, when available, the .csv conversion provided by the source).

Anyway, if interested: the list of all the location as of 2011 is within the ZIP file Localita_2011_Point.csv from "Località italiane (shp)"

Selected the ZIP file containing the set of files WGS 84 UTM Zona 32n, latest available as of 2020-12-06: 2011

Release date and timeframe coverage

The collated dataset was released on 2020-12-06.

No timeframe coverage information available (the "localita" file is stated by ISTAT as updated at 2011).

Acknowledgements

Thanks to EEA and ISTAT for publishing the data

Inspiration

Connecting different data points to identify potential correlations, as part of my knowledge update/learning process (and to complement my other publication activities).

As part of a long-term publishing project (started in 2015 at Expo2015 in Milan), routinely share data that collect along my writing journey- generally via articles on my website on business and social change.

Overview

This dataset is the repository for the following paper submitted to Data in Brief:

Kempf, M. A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19. Data in Brief (submitted: December 2023).

The Data in Brief article contains the supplement information and is the related data paper to:

Kempf, M. Climate change, the Arab Spring, and COVID-19 - Impacts on landcover transformations in the Levant. Journal of Arid Environments (revision submitted: December 2023).

Description/abstract

The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This dataset uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant.

Folder structure

The main folder after download contains all data, in which the following subfolders are stored are stored as zipped files:

“code” stores the above described 9 code chunks to read, extract, process, analyse, and visualize the data.

“MODIS_merged” contains the 16-days, 250 m resolution NDVI imagery merged from three tiles (h20v05, h21v05, h21v06) and cropped to the study area, n=510, covering January 2001 to December 2022 and including January and February 2023.

“mask” contains a single shapefile, which is the merged product of administrative boundaries, including Jordan, Lebanon, Israel, Syria, and Palestine (“MERGED_LEVANT.shp”).

“yield_productivity” contains .csv files of yield information for all countries listed above.

“population” contains two files with the same name but different format. The .csv file is for processing and plotting in R. The .ods file is for enhanced visualization of population dynamics in the Levant (Socio_cultural_political_development_database_FAO2023.ods).

“GLDAS” stores the raw data of the NASA Global Land Data Assimilation System datasets that can be read, extracted (variable name), and processed using code “8_GLDAS_read_extract_trend” from the respective folder. One folder contains data from 1975-2022 and a second the additional January and February 2023 data.

“built_up” contains the landcover and built-up change data from 1975 to 2022. This folder is subdivided into two subfolder which contain the raw data and the already processed data. “raw_data” contains the unprocessed datasets and “derived_data” stores the cropped built_up datasets at 5 year intervals, e.g., “Levant_built_up_1975.tif”.

Code structure

1_MODIS_NDVI_hdf_file_extraction.R

This is the first code chunk that refers to the extraction of MODIS data from .hdf file format. The following packages must be installed and the raw data must be downloaded using a simple mass downloader, e.g., from google chrome. Packages: terra. Download MODIS data from after registration from: https://lpdaac.usgs.gov/products/mod13q1v061/ or https://search.earthdata.nasa.gov/search (MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, last accessed, 09th of October 2023). The code reads a list of files, extracts the NDVI, and saves each file to a single .tif-file with the indication “NDVI”. Because the study area is quite large, we have to load three different (spatially) time series and merge them later. Note that the time series are temporally consistent.

2_MERGE_MODIS_tiles.R

In this code, we load and merge the three different stacks to produce large and consistent time series of NDVI imagery across the study area. We further use the package gtools to load the files in (1, 2, 3, 4, 5, 6, etc.). Here, we have three stacks from which we merge the first two (stack 1, stack 2) and store them. We then merge this stack with stack 3. We produce single files named NDVI_final_*consecutivenumber*.tif. Before saving the final output of single merged files, create a folder called “merged” and set the working directory to this folder, e.g., setwd("your directory_MODIS/merged").

3_CROP_MODIS_merged_tiles.R

Now we want to crop the derived MODIS tiles to our study area. We are using a mask, which is provided as .shp file in the repository, named "MERGED_LEVANT.shp". We load the merged .tif files and crop the stack with the vector. Saving to individual files, we name them “NDVI_merged_clip_*consecutivenumber*.tif. We now produced single cropped NDVI time series data from MODIS. The repository provides the already clipped and merged NDVI datasets.

4_TREND_analysis_NDVI.R

Now, we want to perform trend analysis from the derived data. The data we load is tricky as it contains 16-days return period across a year for the period of 22 years. Growing season sums contain MAM (March-May), JJA (June-August), and SON (September-November). December is represented as a single file, which means that the period DJF (December-February) is represented by 5 images instead of 6. For the last DJF period (December 2022), the data from January and February 2023 can be added. The code selects the respective images from the stack, depending on which period is under consideration. From these stacks, individual annually resolved growing season sums are generated and the slope is calculated. We can then extract the p-values of the trend and characterize all values with high confidence level (0.05). Using the ggplot2 package and the melt function from reshape2 package, we can create a plot of the reclassified NDVI trends together with a local smoother (LOESS) of value 0.3.To increase comparability and understand the amplitude of the trends, z-scores were calculated and plotted, which show the deviation of the values from the mean. This has been done for the NDVI values as well as the GLDAS climate variables as a normalization technique.

5_BUILT_UP_change_raster.R

Let us look at the landcover changes now. We are working with the terra package and get raster data from here: https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 03. March 2023, 100 m resolution, global coverage). Here, one can download the temporal coverage that is aimed for and reclassify it using the code after cropping to the individual study area. Here, I summed up different raster to characterize the built-up change in continuous values between 1975 and 2022.

6_POPULATION_numbers_plot.R

For this plot, one needs to load the .csv-file “Socio_cultural_political_development_database_FAO2023.csv” from the repository. The ggplot script provided produces the desired plot with all countries under consideration.

7_YIELD_plot.R

In this section, we are using the country productivity from the supplement in the repository “yield_productivity” (e.g., "Jordan_yield.csv". Each of the single country yield datasets is plotted in a ggplot and combined using the patchwork package in R.

8_GLDAS_read_extract_trend

The last code provides the basis for the trend analysis of the climate variables used in the paper. The raw data can be accessed https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 9th of October 2023). The raw data comes in .nc file format and various variables can be extracted using the [“^a variable name”] command from the spatraster collection. Each time you run the code, this variable name must be adjusted to meet the requirements for the variables (see this link for abbreviations: https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary, last accessed 09th of October 2023; or the respective code chunk when reading a .nc file with the ncdf4 package in R) or run print(nc) from the code or use names(the spatraster collection). Choosing one variable, the code uses the MERGED_LEVANT.shp mask from the repository to crop and mask the data to the outline of the study area.From the processed data, trend analysis are conducted and z-scores were calculated following the code described above. However, annual trends require the frequency of the time series analysis to be set to value = 12. Regarding, e.g., rainfall, which is measured as annual sums and not means, the chunk r.sum=r.sum/12 has to be removed or set to r.sum=r.sum/1 to avoid calculating annual mean values (see other variables). Seasonal subset can be calculated as described in the code. Here, 3-month subsets were chosen for growing seasons, e.g. March-May (MAM), June-July (JJA), September-November (SON), and DJF (December-February, including Jan/Feb of the consecutive year).From the data, mean values of 48 consecutive years are calculated and trend analysis are performed as describe above. In the same way, p-values are extracted and 95 % confidence level values are marked with dots on the raster plot. This analysis can be performed with a much longer time series, other variables, ad different spatial extent across the globe due to the availability of the GLDAS variables.

(9_workflow_diagramme) this simple code can be used to plot a workflow diagram and is detached from the actual analysis.

Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Project administration, and Funding acquisition: Michael

These data include geospatial files (shapefiles and orthorectified raster images) and an input hydrograph (csv) for a 1-dimensional unsteady hydrologic model. Shapefiles consist of active channel boundarys and channel centerlines of six reaches of the LCR beginning ~4.5 km above Grand Falls, AZ, and ending ~12.8 km downstream from Cameron, AZ. These reaches are (1) the ~4.5 km above Grand Falls reach, (2) the 1.5km below Grand Falls reach, (3) the ~18.8 km Black Falls reach, (4) the ~16.5 km above Cameron reach, (5) the ~4.7 km Cameron to Moenkopi reach, and (6) the ~8.1 km below Moenkopi reach. Raster images consist of orthorectified aerial photograph mosaics between 1933/34 and 1992. Scans of the images were acquired from either the National Archives and Records Administration (NARA) or the USGS Earth Resources Observation and Science Center (EROS). The input hydrograph for the hydrologic model consists of flood data from March 1954 as measured at the Grand Falls stream gage.

GIS shapefiles, python script, and .csv files for Evidence of Topographic Change Recorded by Lava Flows at Atete and Aruru Coronae on Venus. Submitted to JGR: Planets

Abstract

This dataset was derived by the Bioregional Assessment Programme. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This dataset converts the original Digital Atlas of Australian Soils (GUID: 9e7d2f5b-ff51-4f0f-898a-a55be8837828) shapefile into the Australian soil classification, as per data from Conversion of the Atlas of Australian Soils to the Australian Soil Classification (GUID: 295707d5-2774-4ca5-a539-6c0426bbd662). A Layer file is also supplied using the RGB colour reference table, also found in the Conversion of the Atlas of Australian Soils to the Australian Soil Classification dataset.

Purpose

Provides a spatial and cartographic representation of the Digital Atlas of Australian Soils shapefile into the new Australian soil classification.

Dataset History

From the Conversion of the Atlas of Australian Soils to the Australian Soil Classification dataset (GUID: 295707d5-2774-4ca5-a539-6c0426bbd662) the file asclut.txt was converted to .csv format and field headings added (MAP_UNIT, SOIL_CODE, SOIL_SYMBOL, SOIL).

This csv file (asclut.csv) was joined to the Digital Atlas of Australian Soils (GUID: 9e7d2f5b-ff51-4f0f-898a-a55be8837828), soilAtlas2M shapefile on the common 'MAP_UNIT' field. The resulting join was saved as 'soilAtlas2M_ASC_Conversion.shp'

The symbology of this shapefile was updated by matching the RGB values provided in the 'asc_colours.xls' spreadsheet from the Conversion of the Atlas of Australian Soils to the Australian Soil Classification dataset (GUID: 295707d5-2774-4ca5-a539-6c0426bbd662) to the 'SOIL' field. A Layer File was created 'soilAtlas2M_ASC_Conversion.lyr'

Dataset Citation

Bioregional Assessment Programme (2015) Spatial Data Conversion of the Atlas of Australian Soils to the Australian Soil Classification v01. Bioregional Assessment Derived Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/6f804e8b-2de9-4c88-adfa-918ec327c32f.

Dataset Ancestors

• Derived From Digital Atlas of Australian Soils

• Derived From Conversion of the Atlas of Australian Soils to the Australian Soil Classification

The Randolph Glacier Inventory (RGI) is a global set of glacier outlines

GAVIA_main_data_table.csv - This comma-separated text file contains the 27,723 alien bird records that form the core of the Global AVian Invasions Atlas (GAVIA) project. These records represent 971 species, introduced to 230 countries and administrative areas across all eight biogeographical realms, spanning the period 6000 BCE – AD 2014. The data comprises taxonomic (species-level), spatial (geographic location, realm, land type) and temporal (dates of introduction and spread) components, as well as details relating to the introduction event (how and why the species was introduced, whether or not it is established). Each line of data consists of an individual record concerning a specific alien bird species introduced to a specific location. The data derives from both published and unpublished sources, including atlases, country species lists, peer-reviewed articles, websites and via correspondence with in-country experts.GAVIA_abbreviations.csv - This comma-separated text file describes the abbreviations in the main text of the 'GAVIA_main_data_table.csv'.GAVIA_column_names.csv - This comma-separated text file describes the column heading used in the 'GAVIA_main_data_table.csv'.

GAVIA_references.csv - This comma-separated text file contains the full references referred to in the 'GAVIA_main_data_table.csv' column headed "Reference".GAVIA_rangemaps.zip - This compressed folder (.zip format) contains the species’ range maps stored as one ESRI shapefile per species (n = 362). Within these shapefiles are attribute tables which contain a unique species ID number and binomial which match up to the species ID number and binomial in the 'GAVIA main data table'.

Tiff files: Maps of Above Ground Biomass change (2019-2020) over the study region near Iñapari, Peru, derived from the texture of the NIR band for SPOT-7 (SPOT_DeltaAGB_Map), PlanetScope (PlanetScope_DeltaAGB_Map.tif) and Sentinel-2 (Sentinel2_DeltaAGB_Map.tif) data for a 1-ha resolution.QML file contains the style for the biomass change maps. Shapefile contains location of four selectively logged plots.CSV file contains data on observed changes in these four plots, obtained by TLS and manual inventory.

The HANZE dataset covers riverine, pluvial, coastal and compound floods that have occurred in 42 European countries between 1870 and 2020. The data was collected by extensive data-collection from more than 800 sources ranging from news reports through government databases to scientific papers. The dataset includes 2521 events characterized by at least one impact statistic: area inundated, fatalities, persons affected or economic loss. Economic losses are presented both in the original currencies and price levels as well as inflation and exchange-rate adjusted to 2020 value of the euro. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union’s Nomenclature of Territorial Units for Statistics (NUTS), level 3. Daily start and end dates, information on causes of the event, notes on data quality issues or associated non-flood impacts, and full bibliography of each record supplement the dataset. Apart from the possibility to download the data, the database can be viewed, filtered and visualized online: https://naturalhazards.eu. The dataset is designed to be complimentary to HANZE-Exposure, a high-resolution model of historical exposure changes (such as population and asset value), and be easily usable in statistical and spatial analyses.

The dataset contains the following files (CSV comma-delimited, UTF8, and ESRI shapefiles in zipped folders)

HANZE flood events database

HANZE_events.csv - Flood event data

HANZE_references.csv - List of all references

HANZE_events_regions_2010.zip - Flood event data as GIS file (regions v2010)

HANZE_events_regions_2021.zip - Flood event data as GIS file (regions v2021)

Supplementary data

S1_countries_codes_and_names.csv - Country codes/names

S2_regions_codes_and_names_v2010.csv - Region codes/names, v2010

S3_regions_codes_and_names_v2021.csv - Region codes/names, v2021

S4_list_of_all_currencies_by_country.csv - Data on all currencies used in the study area since 1870

S5_currency_conversion_rates.csv - Conversion rates applied to compute losses in 2020 euros

S6_GDP_deflators_by_country.csv - Gross domestic product deflator by country, 1870-2020

S7_floods_removed_from_HANZE.csv - Flood events in HANZE v1, which were excluded from v2

Regions_v2010_simplified.zip - Map of subnational regions used in the database, v2010

Regions_v2021_simplified.zip - Map of subnational regions used in the database, v2021

Note: this is a minor update of the original upload. It corrects the erroneous rendering of NUTS regions for event 2751, fixes some geometry problems with the GIS files and makes some small changes to the flood data (2 events were added and the regional codes for Kosovo in version 2021 were modified based on the upcoming NUTS 2024 classification).

This layer indicates the location of the observed stream temperature records used for the NorWeST database summaries. NorWeST summer stream temperature scenarios were developed for all rivers and streams in the western U.S. from the greater than 20,000 stream sites in the NorWeST database where mean August stream temperatures were recorded. The resulting dataset includes stream lines (NorWeST_PredictedStreams) and associated mid-points NorWest_TemperaturePoints) representing 1 kilometer intervals along the stream network. Stream lines were derived from the 1:100,000 scale NHDPlus dataset (USEPA and USGS 2010; McKay et al. 2012). Shapefile extents correspond to NorWeST processing units, which generally relate to 6 digit (3rd code) hydrologic unit codes (HUCs) or in some instances closely correspond to state borders. The line and point shapefiles contain identical modeled stream temperature results. The two feature classes are meant to complement one another for use in different applications. In addition, spatial and temporal covariates used to generate the modeled temperatures are included in the attribute tables at https://www.fs.usda.gov/rm/boise/AWAE/projects/NorWeST/ModeledStreamTemperatureScenarioMaps.shtml. The NorWeST NHDPlusV1 processing units include: Salmon, Clearwater, Spokoot, Missouri Headwaters, Snake-Bear, MidSnake, MidColumbia, Oregon Coast, South-Central Oregon, Upper Columbia-Yakima, Washington Coast, Upper Yellowstone-Bighorn, Upper Missouri-Marias, and Upper Green-North Platte. The NorWeST NHDPlusV2 processing units include: Lahontan Basin, Northern California-Coastal Klamath, Utah, Coastal California, Central California, Colorado, New Mexico, Arizona, and Black Hills.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

This is the ONS Postcode Directory (ONSPD) for the United Kingdom as at February 2023 in Comma Separated Variable (CSV) and ASCII text (TXT) formats. This file contains the multi CSVs so that postcode areas can be opened in MS Excel. To download the zip file click the Download button. The ONSPD relates both current and terminated postcodes in the United Kingdom to a range of current statutory administrative, electoral, health and other area geographies. It also links postcodes to pre-2002 health areas, 1991 Census enumeration districts for England and Wales, 2001 Census Output Areas (OA) and Super Output Areas (SOA) for England and Wales, 2001 Census OAs and SOAs for Northern Ireland and 2001 Census OAs and Data Zones (DZ) for Scotland. It now contains 2021 Census OAs and SOAs for England and Wales. It helps support the production of area based statistics from postcoded data. The ONSPD is produced by ONS Geography, who provide geographic support to the Office for National Statistics (ONS) and geographic services used by other organisations. The ONSPD is issued quarterly. (File size - 234 MB)NOTE: The 2022 ONSPDs included an incorrect update of the ITL field with two LA changes in Northamptonshire. This error has been corrected from the February 2023 ONSPD.NOTE: There was an issue with the originally published file where some change orders yet to be included in OS Boundary-LineÔ (including The Cumbria (Structural Changes) Order 2022, The North Yorkshire (Structural Changes) Order 2022 and The Somerset (Structural Changes) Order 2022) were mistakenly implemented for terminated postcodes. Version 2 corrects this, so that ward codes E05014171–E05014393 are not yet included. Please note that this product contains Royal Mail, Gridlink, LPS (Northern Ireland), Ordnance Survey and ONS Intellectual Property Rights.