Displaying 1 to 18 from 18 results

3d-tiles - Specification for streaming massive heterogeneous 3D geospatial datasets :earth_americas:

  •    Batchfile

A building CAD model is fused with photogrammetry data using 3D Tiles, data courtesy of Bentley Systems. 3D Tiles is an open specification for sharing, visualizing, fusing, and interacting with massive heterogenous 3D geospatial content across desktop, web, and mobile applications.

geobr - Easy access to official spatial data sets of Brazil in R and Python

  •    R

geobr is a computational package to download official spatial data sets of Brazil. The package includes a wide range of geospatial data in geopackage format (like shapefiles but better), available at various geographic scales and for various years with harmonized attributes, projection and topology (see detailed list of available data sets below). The package is currently available in R and Python.

3d-tiles - Specification for streaming massive heterogeneous 3D geospatial datasets :earth_americas:


Specification for streaming massive heterogeneous 3D geospatial datasets. 3D Tiles has entered the Open Geospatial Consortium (OGC) Community Standard process.

rgee - Google Earth Engine for R

  •    R

Google Earth Engine is a cloud-based platform that lets users access a petabyte-scale archive of remote sensing data and run geospatial analysis on Google's infrastructure. Currently, Google offers support only for Python and JavaScript. rgee will fill the gap starting to provide support to R!. Below you will find the comparison between the syntax of rgee and the two other Google-supported client libraries. Take into account that the Python PATH you set must have installed the Earth Engine Python API and numpy. The use of miniconda/anaconda is mandatory for Windows users, Linux and MacOS users could also use virtualenv. See reticulate documentation for more details.

earthpy - A package built to support working with spatial data using open source python

  •    Python

EarthPy makes it easier to plot and manipulate spatial data in Python. EarthPy's design was inspired by the raster and sp package functionality available to R users.

USAboundaries - Historical and Contemporary Boundaries of the United States of America

  •    R

This R package includes contemporary state, county, and Congressional district boundaries, as well as zip code tabulation area centroids. It also includes historical boundaries from 1629 to 2000 for states and counties from the Newberry Library's Atlas of Historical County Boundaries, as well as historical city population data from Erik Steiner's "United States Historical City Populations, 1790-2010." The package has some helper data, including a table of state names, abbreviations, and FIPS codes, and functions and data to get State Plane Coordinate System projections as EPSG codes or PROJ.4 strings.You can install this package from CRAN.

arcgis-python-api - Documentation and samples for ArcGIS Python API

  •    Python

Anyone and everyone is welcome to contribute. Please see our guidelines for contributing. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.


  •    Jupyter

This is a forkable set of teaching materials for teaching biologists how to work with data through programming, database management and computing more generally. This repository contains the complete teaching materials (excluding exams and answers to assignments) and website for a university style and self-guided course teaching computational data skills to biologists. The course is designed to work primarily as a flipped classroom, with students reading and viewing videos before coming to class and then spending the bulk of class time working on exercises with the teacher answering questions and demoing the concepts.

GeoStats.jl - An extensible framework for high-performance geostatistics in Julia

  •    Julia

GaussianProcesses.jl — Gaussian processes (the method) and Simple Kriging are essentially two names for the same concept. The derivation of Kriging estimators, however; does not require distributional assumptions. It is a beautiful coincidence that for multivariate Gaussian distributions, Simple Kriging gives the conditional expectation. Matheron and other important geostatisticians have generalized Gaussian processes to more general random fields with locally-varying mean and for situations where the mean is unknown. GeoStats.jl includes Gaussian processes as a special case as well as other more practical Kriging variants, see the Gaussian processes example. MLKernels.jl — Spatial structure can be represented in many different forms: covariance, variogram, correlogram, etc. Variograms are more general than covariance kernels according to the intrinsically stationary property. This means that there are variogram models with no covariance counterpart. Furthermore, empirical variograms can be easily estimated from the data (in various directions) with an efficient procedure. GeoStats.jl treats variograms as first-class objects, see the Variogram modeling example.

R-GIS-tutorial - Spatial data in R: using R as a GIS

  •    R

A tutorial to perform basic operations with spatial data in R, such as importing and exporting data (both vectorial and raster), plotting, analysing and making maps.

quickmapr - An R package for quickly mapping and navigating spatial data

  •    R

There are many packages that already exist or are in active development that support the visualization of spatial data in R. However, there seems to be a gap for those that need to quickly view, compare, and explore the results of a given spatial analysis. The current thinking behind quickmapr is to allow for quick visualization of sp and raster objects. Functionality for the current release is for easy mapping of multiple layers, simple zooming, panning, labelling, and identifying. These tools are intended for use within an active spatial analysis workflow and not for production quality maps.

getSpatialData - An R package 📦 making it easy to query, preview, download and preprocess multiple kinds of spatial data 🛰 via R

  •    R

getSpatialData is an R package in an early development stage that ultimately aims to provide homogeneous function bundles to query, download, prepare and transform various kinds of spatial datasets from open sources, e.g. Satellite sensor data, higher-level environmental data products etc. It supports both sf and sp classes as AOI inputs (see set_aoi in available functions). Due to the early development stage, the included functions and their concepts could be removed or changed in some cases. For all public functions documentation is available. See also the list of data sources that are or will be implemented.

s2 - R bindings for Google's s2 library for geometry on the sphere

  •    C

The package (master branch) currently passes R CMD check without any errors and warnings on Linux, OSX and Windows. Only a minor subset of the C++ library is wrapped at the moment. Simple R data structures such as plain matrices and lists are used. The API is not stable and changes should be expected. The core C++ code is in src/geometry. This code is a slightly modified copy of the corresponding directory in this repo. (The subdirectories python and test are removed since they aren't needed by the R package.) The modifications to the original code are minor edits to satisfy the R package checker, and no new functionality is introduced at the C++ level.

spatstat - Development version of 'spatstat' package

  •    R

This repository holds a copy of the current development version of the contributed R-package spatstat. This development version is more recent than the official release of spatstat on CRAN. Each official release of spatstat has a version number like 1.2-3 while the development version has a version number like 1.2-3.004 (which R recognises as a later version). Official releases occur every 8 weeks (the minimum time permitted by CRAN policies) while the development code is updated almost every day.

pareto - Spatial Containers, Pareto Fronts, and Pareto Archives

  •    C++

While most problems need to simultaneously organize objects according to many criteria, associative containers can only index objects in a single dimension. This library provides a number of containers with optimal asymptotic complexity to represent multi-dimensional associative containers. These containers are useful in many applications such as games, maps, nearest neighbor search, range search, compression algorithms, statistics, mechanics, graphics libraries, database queries, finance, multi-criteria decision making, optimization, machine learning, hyper-parameter tuning, approximation algorithms, networks, routing algorithms, robust optimization, design, and systems control.

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