grace-amondi / ordinary_kriging

Geostatistical Prediction with ordinary kriging

Ordinary Kriging

Introduction

Geostatistical Prediction with ordinary kriging Ordinary Kriging is a spatial estimation method where the error variance is minimized. This error variance is called the kriging variance. It is based on the configuration of the data and on the variogram. For example, say we are studying copper concentration in soil, your training data is point data with attribute information..say copper concentration at each point.The test data contains just point data...without copper concentration.The train data is used to create a variogram which predicts the copper concentration for the test data. Try Demo or view demo on youtube.

According to sakitam-gis,the various variogram models can be interpreted as kernel functions for 2-dimensional coordinates a, b and parameters nugget, range, sill and A. Reparameterized as a linear function, with w = [nugget, (sill-nugget)/range], this becomes:

• Gaussian: k(a,b) = w[0] + w[1] * ( 1 - exp{ -( ||a-b|| / range )2 / A } )
• Exponential: k(a,b) = w[0] + w[1] * ( 1 - exp{ -( ||a-b|| / range ) / A } )
• Spherical: k(a,b) = w[0] + w[1] * ( 1.5 * ( ||a-b|| / range ) - 0.5 * ( ||a-b|| / range )3 )

The variance parameter α of the prior distribution for w should be manually set, according to:

• w ~ N(w|0, αI)

Using the fitted kernel function hyperparameters and setting K as the Gram matrix, the prior and likelihood for the gaussian process become:

• y ~ N(y|0, K)
• t|y ~ N(t|y, σ2I)

---

Run application

Clone application

git clone https://github.com/Grace-Amondi/ordinary_kriging.git

Move into the ordinary_kriging directory

cd ordinary_kriging

Change .env.example to .env and Set your mapbox access token.Install node modules

npm install

Run application

npm start

Open application at http://localhost:1234

---

Build for production

To build the app for production,

npm run build

Navigate to /dist folder and deploy to surge or github pages

---

Resources Used

Mapbox GL

Mapbox GL JS is a JavaScript library used to render interactive maps. This application uses Mapbox GL to create a map object, display geojson data, create layers from the datasets and create layer groups from the layers.

Turf JS

Turf JS provides a set of submodules for advanced geospatial analysis on the browser. This application uses turf js to create point features, append attribute data to point data and create feature collections from the point data

Mapbox GL plugins

mapbox-gl-controls

mapbox-gl-controls provides an array of controls that can be added to the map to enhance interactivity. This application uses mapbox-gl-controls's RulerControl,CompassControl,ZoomControl and AroundControl submodules.

Javascript FileReader

Javascript FileReader enables asynchronous reading of contents of files (or raw data buffers) stored on the user's computer, using File or Blob objects to specify the file or data to read.This was used in reading of training and test geojson data.

FileSaver

FileSaver enables saving files on the client-side which is exactly what it was used for in this application. With the saveAs() function you are good to go.

Parcel

Parcel is a web application bundler. It has been used to bundle this application into a minified version.Parcel also converts JS assets to ES5.

Materialize

Materialize has been used to design the app's beautiful user interface with mobile responsiveness, grids and cards.

Geojson-Generator

Geojson-Generator is a tool that enables generation of random geojson point data within a specified bounding box. This application used it to generate random numeric point data for testing.

ordinary_kriging's Issues

Assigned bounds twice

ordinary_kriging/js/index.js

Line 322 in cfd806d

var bounds = new mapboxgl.LngLatBounds();