We’ve been asked to create measures of communities that are “walkable” for several projects. While there is no standard definition of what makes a community “walkable”, and the definition of “walkability” can differ from person to person, we thought an indicator that explores the total length of available sidewalks relative to the total length of streets in a community could be a good place to start. In this blog post, we describe how we used open data from SPC and Allegheny County to create a new measure for how “walkable” a community is. We wanted to create a ratio of the length of a community’s sidewalks to the length of a community’s streets as a measure of pedestrian infrastructure. A ratio of 1 would mean that a community has an equal number of linear feet of sidewalks and streets. A ratio of about 2 would mean that a community has two linear feet of sidewalk for every linear foot of street. In other words, every street has a sidewalk on either side of it. In creating a measure of the ratio of streets to sidewalks, we had to do a little bit of data cleanup. Much of this was by trial and error, ground-truthing the data based on our personal experiences walking in different neighborhoods. Since street data was not shared as open data by many counties in our region either on PASDA or through the SPC open data portal, we limited our analysis of “walkability” to Allegheny County.

In looking at the sidewalk data table and map, we noticed that trails were included. While nice to have in the data, we wanted to exclude these two features from the ratio. We did this to avoid a situation where a community that had few sidewalks but was in the same blockgroup as a park with trails would get “credit” for being more “walkable” than it actually is according to our definition. We did this by removing all segments where “Trail” was in the “Type_Name” field.

We also used a similar tabular selection method to remove crosswalks from the sidewalk data “Type_Name”=”Crosswalk.” We kept the steps in the dataset along with the sidewalks.

In the street data obtained from Allegheny County’s GIS department, we felt like we should try to exclude limited-access highway segments from the analysis, since pedestrians are prohibited from using them, and their presence would have reduced the sidewalk/street ratio in communities where they are located. We did this by excluding street segments whose values in the “FCC” field (designating type of street) equaled “A11” or “A63.” We also removed trails from this dataset by excluding those classified as “H10.” Since documentation was sparse, we looked to see how these features were classified in the data to determine which codes to exclude.

After running the data initially, we also realized that excluding alleyways from the calculations also could improve the accuracy of our results. Some of the communities with substantial pedestrian infrastructure have alleyways, and including them would make them appear to be less-”walkable” in our indicator. We removed these from the dataset by removing records with a value of “Aly” or “Way” in the “St_Type” field. We also excluded streets where the word “Alley” appeared in the street name, or “St_Name” field.

The full methodology used for this dataset is captured in our blog post, and we have also included the sidewalk and street data used to create the ratio here as well.

This dataset provides an indicator of dependency ratios for OECD Functional Urban Areas (FUAs) and cities.

   <h3>Data sources and methodology</h3>
   <p align="justify">
   Dependency ratios are derived from population by age and sex data collected at the level of small administrative units (e.g. municipalities) and aggregated at the FUA and city level. The correspondence table between SAUs and FUAs/cities is available at <a href=https://stats.oecd.org/wbos/fileview2.aspx?IDFile=21612592-67a6-4718-baf5-23c7f832ffed>this link</a>.<br /><br />
   When population by age and sex data is not available at such granular level, FUA and city level indicators are estimated by adjusting the regional (OECD TL3 regions) values to the FUA and city boundaries, based on the population distribution in each region. Regional values (population by age and sex) in TL3 regions are used as data inputs and combined with gridded total population data <a href=https://doi.org/10.2760/098587>(European Commission, GHSL Data Package 2023)</a>. FUA and city boundaries are intersected with TL3 borders and coefficients are computed for each region, based on the share of the regional population that lives within the FUA/city. These coefficients are then applied to the variables of interest (e.g. population by age groups) and allocated to the FUA/city. In case several regions intersect the FUA/city, the adjusted values of intersecting regions are summed. This approach assumes that the variables of interest have the same spatial distribution as the total population. Therefore, the modelled indicators should be interpreted with caution.<br /><br />
   </p>

   <h3>Defining FUAs and cities</h3>
   <p align="justify">The OECD, in cooperation with the EU, has developed a harmonised <a href="https://www.oecd.org/en/data/datasets/oecd-definition-of-cities-and-functional-urban-areas.html">definition of functional urban areas</a> (FUAs) to capture the economic and functional reach of cities based on daily commuting patterns <a href=https://doi.org/10.1787/9789264174108-en>(OECD, 2012)</a>. FUAs consist of:
   <ol>
   <li><b>A city</b> – defined by urban centres in the degree of urbanisation, adapted to the closest local administrative units to define a city.</li>
   <li><b>A commuting zone</b> – including all local areas where at least 15% of employed residents work in the city.</li>
   </ol>
   The delineation process includes:
   <ul>
   <li>Assigning municipalities surrounded by a single FUA to that FUA.</li>
   <li>Excluding non-contiguous municipalities.</li>
   </ul>
   The definition identifies 1 285 FUAs and 1 402 cities in all OECD member countries except Costa Rica and three accession countries.</p>
   <h3>Cite this dataset</h3>
   <p>OECD Regions, cities and local areas database (<a href=http://data-explorer.oecd.org/s/1ds>Dependency ratio - Cities and FUAs</a>), <a href="http://oe.cd/geostats">http://oe.cd/geostats</a></p>
   <h3>Further information</h3>
   <p align="justify">For any question or comment, please write to <a href="mailto:RegionStat@oecd.org">RegionStat@oecd.org</a><br /><br />FUA and City Statistics can be further explored with the interactive <a href="https://regions-cities-atlas.oecd.org">OECD Regions and Cities Statistical Atlas</a> web-tool.</p>

The product data are six statistics that were estimated for the chemical concentration of lithium in the soil C horizon of the conterminous United States. The estimates are made at 9998 locations that are uniformly distributed across the conterminous United States. The six statistics are the mean for the isometric log-ratio transform of the concentrations, the equivalent mean for the concentrations, the standard deviation for the isometric log-ratio transform of the concentrations, the probability of exceeding a concentration of 55 milligrams per kilogram, the 0.95 quantile for the isometric log-ratio transform of the concentrations, and the equivalent 0.95 quantile for the concentrations. Each statistic may be used to generate a statistical map that shows an attribute of the distribution of lithium concentration.

This dataset contains spatial boundaries for Bonus Plot Ratio Plans relating to the City of Perth Planning Scheme No.2The Maximum Bonus Plot Ratio Plan shows the total maximum bonus plot ratio that can be granted on a specific lot. This is either 20% or 50%.Bonus plot ratio may be granted under a single category or a combination of Special Residential, Residential, Heritage and Public Facilities.Definition under Schedule 4 “means the maximum percentage increase in the maximum plot ratio which is specified for a lot or part of a lot by the Maximum Bonus Plot Ratio Plan”;Please see https://perth.wa.gov.au/develop/planning-framework/planning-schemes and https://perth.wa.gov.au/develop/planning-framework/planning-policies-and-precinct-plans for more information regarding the City of Perth Planning Schemes.

Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive: •\tIncoming short-wave radiation on a sloping surface •\tShort-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface) •\tIncoming long-wave radiation •\tOutgoing long-wave radiation •\tNet long-wave radiation •\tNet radiation •\tSky view factor All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane.

The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view.

The monthly data in this collection are available at 1 arcsecond resolution as 1x1 degree tiles in ESRI float grid format. 813 tiles make up the extent of Australia. The 1 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18731 .

The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces, by aggregating the cells in a 3x3 window and taking the mean value.

The 3 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18732 Lineage: Source data 1. 1 arcsecond SRTM-derived Smoothed Digital Elevation Model (DEM-S; ANZCW0703014016) 2. Aspect derived from the 1 arcsecond SRTM DEM-S 3. Slope derived from the 1 arcsecond SRTM DEM-S 4. Monthly cloud cover fraction (Jovanovic et al., 2011) 5. Monthly albedo derived from AVHRR (Donohue et al., 2010) 6. Monthly minimum and maximum air temperature (Bureau of Meteorology) 7. Monthly vapour pressure (Bureau of Meteorology) 8. Monthly fractional cover (Donohue et al., 2010) 9. Monthly black-sky and white-sky albedo from MODIS (MCD43A3, B3) (Paget and King, 2008; NASA LP DAAC, 2013) 10. Measurements of daily sunshine hours, 9 am and 3pm cloud cover, and daily solar radiation from meteorological stations around Australia (Bureau of Meteorology)

Solar radiation model Solar radiation was calculated using the SRAD model (Wilson and Gallant, 2000), which accounts for: \tAnnual variations in sun-earth distance \tSolar geometry based on latitude and time of year \tThe orientation of the land surface relative to the sun \tShadowing by surrounding topography \tClear-sky and cloud transmittance \tSunshine fraction (cloud-free fraction of the day) in morning and afternoon \tSurface albedo \tThe effects of surface temperature on outgoing long-wave radiation, which is modulated by incoming radiation and moderated by vegetation cover \tAtmospheric emissivity based on vapour pressure

All input parameters were long-term averages for each month, i.e., monthly climatologies of cloud cover, air temperature, vapour pressure, fractional cover, AVHRR albedo and MODIS albedo.

Circumsolar coefficient was fixed both spatially and temporally at 0.25, while clear sky atmospheric transmissivity and cloud transmittance were varied. Transmittance measures the fraction of radiation passing through a material (air or clouds in this case), while transmissivity measures that fraction for a specified amount of material. SRAD uses a transmittance parameter for cloud, representing an average of all cloud types during cloudy periods, and a transmissivity parameter for clear sky so that the transmittance can vary with the position of the sun in the sky and hence the thickness of atmosphere that radiation passes through on its way to the ground. The clear sky transmissivity τ and cloud transmittance β were calibrated using observed daily radiation and sunshine hours.

References Donohue R. J., McVicar T. R. and Roderick M. L. (2010a). Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386, 186-197, doi:10.1016/j.jhydrol.2010.03.020.

Donohue, R. J., T. R. McVicar, L. Lingtao, and M. L. Roderick (2010b). A data resource for analysing dynamics in Australian ecohydrological conditions, Austral Ecol, 35, 593–594, doi: 10.1111/j.1442-9993.2010.02144.x.

Erbs, D. G., S. A. Klein, and J. A. Duffie (1982), Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Solar Energy, 28(4), 293-302.

Jovanovic, B., Collins, D., Braganza, K., Jakob, D. and Jones, D.A. (2011). A high-quality monthly total cloud amount dataset for Australia. Climatic Change, 108, 485-517.

NASA Land Processes Distributed Active Archive Center (LP DAAC) (2013). MCD43A3, B3. USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota

Paget, M.J. and King, E.A. (2008). MODIS Land data sets for the Australian region. CSIRO Marine and Atmospheric Research Internal Report No. 004. https://remote-sensing.nci.org.au/u39/public/html/modis/lpdaac-mosaics-cmar

Wilson, J.P. and Gallant, J.C. (2000) Secondary topographic attributes, chapter 4 in Wilson, J.P. and Gallant, J.C. Terrain Analysis: Principles and Applications, John Wiley and Sons, New York.

Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive: • Incoming short-wave radiation on a sloping surface • Short-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface) • Incoming long-wave radiation • Outgoing long-wave radiation • Net long-wave radiation • Net radiation • Sky view factor All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane. The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view. The monthly data in this collection are available at 1 arcsecond resolution as single (mosaicked) grids for Australia in TIFF format. The 1 arcsecond tiled data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:9631 . The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces, by aggregating the cells in a 3x3 window and taking the mean value. The 3 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18732

Mean monthly solar radiation was modelled across Australia using topography from the 1 arcsecond resolution SRTM-derived DEM-S and climatic and land surface data. The SRAD model (Wilson and Gallant, 2000) was used to derive: • Incoming short-wave radiation on a sloping surface • Short-wave radiation ratio (shortwave on sloping surface / shortwave on horizontal surface) • Incoming long-wave radiation • Outgoing long-wave radiation • Net long-wave radiation • Net radiation • Sky view factor All radiation values are in MJ/m2/day except for short-wave radiation ratio which has no units. The sky view factor is the fraction of the sky visible from a grid cell relative to a horizontal plane. The radiation values are determined for the middle day of each month (14th or 15th) using long-term average atmospheric conditions (such as cloudiness and atmospheric transmittance) and surface conditions (albedo and vegetation cover). They include the effect of terrain slope, aspect and shadowing (for sun positions at 5 minute intervals from sunrise to sunset), direct and diffuse radiation and sky view. The monthly data in this collection are available at 3 arcsecond resolution as single (mosaicked) grids for Australia in TIFF format. The 3 arcsecond resolution versions of these radiation surfaces have been produced from the 1 arcsecond resolution surfaces, by aggregating the cells in a 3x3 window and taking the mean value. The 1 arcsecond tiled data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:9631 . The 1 arcsecond mosaic data can be found here: https://data.csiro.au/dap/landingpage?pid=csiro:18731

We combine multivariate ratio analysis (MRA) of body measurements and analyses of mitochondrial and nuclear data to examine the status of several species of European paper wasps (Polistes Latreille, 1802) closely related to P. gallicus. Our analyses unambiguously reveal the presence of a cryptic species in Europe, as two distinct species can be recognized in what has hitherto been considered Polistes bischoffi Weyrauch, 1937. One species is almost as light coloured as P. gallicus, and is mainly recorded from Southern Europe and Western Asia. The other species is darker and has a more northern distribution in Central Europe. Both species occur syntopically in Switzerland. Given that the lost lectotype of P. bischoffi originated from Sardinia, we selected a female of the southern species as a neotype. The northern species is described as P. helveticus sp. n. here. We also provide a redescription of P. bischoffi rev. stat. and an identification key including three more closely related species, P. biglumis, P. gallicus and P. hellenicus.

This dataset contains spatial boundaries for Plot Ratio Plans relating to the City of Perth Planning Scheme No.2The Plot Ratio Plan determines the development potential on each lot under the City of Perth's planning authority.Plot ratio is written as a ratio i.e. a site of 1000msq with a plot ratio of 6:1 can develop a maximum of 6000msq of floor space. Therefore the higher the plot ratio of a site the greater its development potential.Definition under Schedule 4 “Plot ratio means the ratio of the floor area of a building to the area of land within the boundaries of the lots on which that building is located;”Please see https://perth.wa.gov.au/develop/planning-framework/planning-schemes and https://perth.wa.gov.au/develop/planning-framework/planning-policies-and-precinct-plans for more information regarding the City of Perth Planning Schemes.

This dataset is about: (Table 5) Mean Sr/Ca ratios of corals. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.825515 for more information.

These data identify the mean annual population growth rate and ratio change in abundance of common raven (Corvus corax; ravens) populations from 1966 through 2018.

The product data are six statistics that were estimated for the chemical concentration of lanthanum in the soil C horizon of the conterminous United States (Smith and others, 2013). The estimates are made at 9998 locations that are uniformly distributed across the conterminous United States. The six statistics are the mean for the isometric log-ratio transform of the concentrations, the equivalent mean for the concentrations, the standard deviation for the isometric log-ratio transform of the concentrations, the probability of exceeding a concentration of 48.8 milligrams per kilogram, the 0.95 quantile for the isometric log-ratio transform of the concentrations, and the equivalent 0.95 quantile for the concentrations. Each statistic may be used to generate a statistical map that shows an attribute of the distribution of lanthanum concentration.

This dataset is about: (Table 2) Mean Cadmium/Calcium ratio of sediment core RC13-229. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.729789 for more information.

With the aim of promoting international comparability, statistics presented on ILOSTAT are based on standard international definitions wherever feasible and may differ from official national figures. This series is based on the 13th ICLS definitions. For time series comparability, it includes countries that have implemented the 19th ICLS standards, for which data are also available in the Work Statistics -- 19th ICLS (WORK) database. The employment-to-population ratio is the number of persons who are employed as a percent of the total of working-age population. For more information, refer to the Rural and Urban Labour Market Statistics (RURBAN) database description.

Definition: The 90/10 decile ratio is a measure of the inequality of distribution. It is determined here in relation to the distribution of equivalised income. It sets the lower limit of the equivalised income of the highest-income decile (= upper limit of the 9. The ratio of the equivalised income of the lowest-income decile. Equivalised income is a weighted per capita income per household member, which is calculated by dividing household net income by the sum of the household weights of persons living in the household. The head of the household is assigned the weight = 1, for the other household members weights of < 1 are used because it is assumed that savings can be achieved through joint management. The new OECD scale is used as a scale of equivalence to determine the respective weights. After that, the head of household is assigned a weight of 1, other household members aged 14 or more a weight of 0.5 and household members under the age of 14 are assigned a weight of 0.3. In order to form the income decile, all persons are sorted according to the level of equivalised income and divided into ten equal groups. The first decile contains the 10 percent with the lowest, the tenth with the highest equivalised income.

Data source:
IT.NRW, Microcensus

DESCRIPTION

Create a model that predicts whether or not a loan will be default using the historical data.

Problem Statement:

For companies like Lending Club correctly predicting whether or not a loan will be a default is very important. In this project, using the historical data from 2007 to 2015, you have to build a deep learning model to predict the chance of default for future loans. As you will see later this dataset is highly imbalanced and includes a lot of features that make this problem more challenging.

Domain: Finance

Analysis to be done: Perform data preprocessing and build a deep learning prediction model.

Content:

Dataset columns and definition:

credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.

purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other").

int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.

installment: The monthly installments owed by the borrower if the loan is funded.

log.annual.inc: The natural log of the self-reported annual income of the borrower.

dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).

fico: The FICO credit score of the borrower.

days.with.cr.line: The number of days the borrower has had a credit line.

revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).

revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).

inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.

delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.

pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).

Steps to perform:

Perform exploratory data analysis and feature engineering and then apply feature engineering. Follow up with a deep learning model to predict whether or not the loan will be default using the historical data.

Tasks:

1. Feature Transformation

Transform categorical values into numerical values (discrete)

1. Exploratory data analysis of different factors of the dataset.

2. Additional Feature Engineering

You will check the correlation between features and will drop those features which have a strong correlation

This will help reduce the number of features and will leave you with the most relevant features

1. Modeling

After applying EDA and feature engineering, you are now ready to build the predictive models

In this part, you will create a deep learning model using Keras with Tensorflow backend

This data release contains a CSV dataset of the likelihood of sapling recruitment based on abundance of seedlings height classes and other stand and site-level factors. Maps accompany the CSV on how the relationship between temperature and relative survival and seedling/sapling abundance varies by size class for ten tree species in the northeastern and midwestern USA, and can be used to highlight areas where survival prospects are relatively better or worse for seedlings of different sizes.

Our research hypothesis is to evaluate the effectiveness of different Bangla text summarization methods compared to the original text ('main'). The data shows that:

• The average length of the main text is 2482.72 characters.
• The average length of the summaries are:
  • sum1: 293.75 characters,
  • sum2: 506.10 characters,
  • sum3: 688.50 characters.

The compression ratio of each summary method (summary length divided by main length) reveals that: - sum1's mean compression ratio is 0.14, - sum2's mean compression ratio is 0.24, and - sum3's mean compression ratio is 0.33.

Notable findings: - sum1 appears to be the shortest summary on average, with a higher degree of compression. - sum2 produces summaries of medium length, while sum3 tends to generate the longest summaries.

Data Gathering and Interpretation: The data can be interpreted to assess which method produces the most concise, yet meaningful, summaries. Researchers can use these findings to evaluate the trade-offs between summary length and completeness of information conveyed.

Metadata information

Full Title Band Ratio Mosaics from Airborne Hyperspectral Data at Aramo, Spain

Abstract

This dataset comprises results from the S34I Project, derived from processing airborne hyperspectral data acquired at the Aramo pilot site in Spain. Spectral Mapping Services (SMAPS Oy) conducted the airborne data acquisition in May 2024 using the Specim AisaFENIX sensor (covering VNIR-SWIR spectral ranges) over 17 flight lines. SMAPS performed geometric correction, radiometric calibration to reflectance, and atmospheric correction of the data. Subsequent processing steps included spectral smoothing with a Savitzky-Golay filter, cloud masking, bad pixel corrections, and hull correction (continuum removal).

Manual processing and interpretation of hyperspectral data is a challenging, time-consuming, and subjective task, necessitating automated or semi-automated approaches. Therefore, we present a semi-automated workflow for large-scale interpretation of hyperspectral data, based on a combination of state-of-the-art methodologies. This dataset results from the calculation of a series of band ratios applied to the images and their subsequent mosaicking into a TIFF file. The mosaics are delivered as georeferenced TIFF files that cover approximately 97 km² with a spatial resolution of 1.2 m per pixel. The NoData value is set to -9999, representing areas of cloud removal or missing flight lines. The projected coordinate system is UTM Zone 30 Northern Hemisphere WGS 1984, EPSG 4326.

Hyperspectral band ratios involve applying mathematical operations (such as division, subtraction, addition, or multiplication) among the reflectance values of different spectral bands. This technique enhances subtle variations in how materials absorb and reflect light across the electromagnetic spectrum. These variations are caused by electronic transitions, vibrations of chemical bonds (including -OH, Si-O, Al-O, and others), and lattice vibrations within the material's crystal structure.

By creating these mathematical combinations, specific absorption features are emphasized, generating unique spectral fingerprints for different materials. However, these fingerprints alone cannot definitively identify a mineral, as different minerals may share similar absorption features due to common chemical bonds or crystal structures. Spectral geologists use band ratios as a tool to highlight potential areas of interest, but they must integrate this information with other geological knowledge and analyses to accurately interpret the mineralogy of an area.

This dataset includes nine spectral band ratios. The mathematical formulas used to calculate each ratio are provided below:

BR1 target Carbonate / Chlorite / Epidote

BR1 = ((C7 + C9) / (C8))

C7= Mean of bands between 2246.6 and 2257.55 nm

C8= Mean of bands between 2339 and 2345 nm

C9= Mean of bands between 2400 and 2410 nm

BR2 target Chlorite

BR2 = ((Cl1 + Cl2) / (Cl2))

Cl1 = Mean of bands between 2191.93 and 2197.4 nm

Cl2 = Mean of bands between 2246.63 and 2257.55 nm

BR3 target Clay

BR3 = ((C1 + C2) / (C2))

C1 = Mean of bands between 1590.32 and 1612.56 nm

C2 = Mean of bands between 2191.93 and 2208.35 nm

BR4 target Dolomite

BR4 = ((C6 + C8) / (C7))

C6= Mean of bands between 2186 and 2191 nm

C7= Mean of bands between 2246.6 and 2257.55 nm

C8= Mean of bands between 2339 and 2345 nm

BR5 target Fe2

BR5 = ((Fe2n + Fe2d) / (Fe2d))

Fe2n = Mean of bands between 721.85 and 742.48 nm

BR6 target Fe3

BR6 = ((Fe3n - Fe3d) / (Fe3n + Fe3d))

Fe3n = Mean of bands between 776.87 to 811.26 nm

Fe3d = = Mean of 3 bands around 610 nm

BR7 target = Kaolinite / clays

BR7 = ((K1 + K2) / (K3 + K4))

K1 = Mean of bands between 2082.27 and 2104.23 nm

K2 = Mean of bands between 2104.23 and 2115.2 nm

K3 = Mean of bands between 2159.07 and 2164.55 nm

K4 = Mean of bands between 2202.88 and 2208.35 nm

BR8 target Kaolinite2 / clays

BR8 = ((K1_2 + K2_2) / (K2_2))

K1_2 = Mean of bands between 2197.4 and 2219.29 nm

K2_2 = Mean of bands between 2159.07 and 2170.03 nm

BR9 target NDVI (Normalized Difference Vegetation Index)

BR9 = ((NIR - Red) / (NIR + Red))

NIR= Mean of bands between 776.87 and 811.26 nm

Red = Mean of bands between 666.87 to 680.6 nm

Keywords Earth Observation, Remote Sensing, Hyperspestral Imaging, Automated Processing, Hyperspectral Data Processing, Mineral Exploration, Critical Raw Materials

Pilot area Aramo

Language

English

URL Zenodo https://zenodo.org/uploads/14193286

Temporal reference

Acquisition date (dd.mm.yyyy) 01.05.2024

Upload date (dd.mm.yyyy) 20.11.2024

Quality and validity

Fromat GeoTiff

Spatial resolution 1.2m

Positional accuracy 0.5m

Coordinate system EPGS 4326

Access and use constrains

Use limitation None

Access constraint None

Public/Private Public

Responsible organisation

Responsible Party Beak Consultants GmbH

Responsible Contact Roberto De La Rosa

Metadata on metadata

Contact Roberto.delarosa@beak.de

Metadata language English

Topsoil nutrient data - total nitrogen (N) concentration (%), C:N ratio and Olsen-Phosphorus (mg/kg). Data is representative of 0 - 15 cm soil depth. Cores from 256 1km x 1km squares across Great Britain were analysed in 2007. For total N concentration (and therefore C:N ratio), a total of 1024 cores were analysed, and for Olsen-P, a total of 1054 cores were analysed. See Emmett et al. 2010 for further details of sampling and methods (http://nora.nerc.ac.uk/id/eprint/5201/1/CS_UK_2007_TR3%5B1%5D.pdf) Estimates of mean values within selected habitats and parent material characteristics across GB were made using Countryside Survey (CS) data from 1978, 1998 and 2007 using a mixed model approach. The estimated means of habitat/parent material combinations are modelled on dominant habitat and parent material characteristics derived from the Land Cover Map 2007 and Parent Material Model 2009, respectively. The parent material characteristic used was that which minimised AIC in each model (see Dataset Documentation). Please see Scott, 2008 for further details of similar statistical analysis (http://nora.nerc.ac.uk/id/eprint/5202/1/CS_UK_2007_TR4%5B1%5D.pdf). Areas, such as urban and littoral rock, are not sampled by CS and therefore have no associated data. Also, in some circumstances sample sizes for particular habitat / parent material combinations were insufficient to estimate mean values.