sample.js

// Geometries
Map.setCenter(-88, 41.8, 9);
// Load Feature Collection of the dissolved boundary of Chicago stored in assets
var chic = ee.FeatureCollection('users/tirthankar25/chicago_diss');
// Load Feature Collection of the neighborhood-scale Chicago data stored in assets
var chicColl = ee.FeatureCollection('users/tirthankar25/chicago');
// Add the layer to the map
// Map.addLayer(Res);
Map.addLayer(chic, {color: 'red'}, 'Chicago dissolved');
// Find the rectangle that emcompasses the southernmost, westernmost, easternmost, and northernmost
// points of the feature
var bound = chic.geometry().bounds();
Map.addLayer(bound, {color: 'yellow'}, 'Bounds');
// Find the polygon covering the extremities of the feature
var convex = chic.geometry().convexHull();
Map.addLayer(convex, {color: 'blue'}, 'Convex Hull');
// Find the area common to two or more features
var intersect = bound.intersection(convex, 100);
Map.addLayer(intersect, {color: 'green'}, 'Bound and convex intersection');
// Find the area encompassing two or more features; number is the maximum error in meters
var union = bound.union(convex, 100);
Map.addLayer(union, {color: 'purple'}, 'Bound and convex union');
// Find the difference between two geometries
var diff=bound.difference(convex, 100);
Map.addLayer(diff, {color: 'purple'}, 'Bound and convex difference');
// Find area of feature
var ar = chic.geometry().area();
print(ar);
// Find length of line geometry (You get zero since this is a polygon)
var length = chic.geometry().length();
print(length);
// Find permeter of feature
var peri = chic.geometry().perimeter();
print(peri);
function performMap(feature) {
 // Reduce number of vertices in geometry; the number is to specify maximum error in meters
 var simple = feature.simplify(1000);
 // Find centroid of geometry
 var center = simple.centroid();
 // Create buffer around geometry; the number represents the width of buffer in meters
 var buff = center.buffer(500);
 // return variable from function
 return buff;
}
// Map function over Feature Collection
var mappedCentroid = chicColl.map(performMap);
Map.addLayer(mappedCentroid, {}, 'Mapped centroids');

// Features
// Create Geometry
var varGeometry = ee.Geometry.Polygon(0, 0, 40, 30, 20, 20, 0, 0);
// Create Feature from Geometry
var varFeature = ee.Feature(varGeometry, {
 Name: ['Feature name', 'Supreme'],
 Size: [500, 1000]
});
// Get values of a property
var arr=varFeature.get('Size');
print(arr);
// Select a subset of properties and rename them
var varFeaturenew = varFeature.select(['Name'], ['Descriptor']);
print(varFeaturenew);

// Images
Map.setCenter(-88, 41.8, 9);
var raw = ee.ImageCollection('MODIS/006/MYD11A2');
var roi = ee.FeatureCollection('users/tirthankar25/chicago');
print(raw);
// Select a band of the image collection using either indexing or band name
var bandSel1 = raw.select(0);
var bandSel2=raw.select('LST_Day_1km');
// Filter the image collection by a date range
var filtered=raw.filterDate('2002-12-30', '2004-4-27');
print(filtered);
// Limit the image collection to the first 50 elements
var limited = raw.limit(50);
print(limited);
print(bandSel1);
// Calculate mean of all images (pixel-by-pixel) in the collection
var mean = bandSel1.mean();
// Calculate image of region of interest
var clipped = mean.clip(roi);
// mathematical operation on image pixels to convert from digital number
// of satellite observations to degree Celsius
var calculate = clipped.multiply(.02).subtract(273.15);
Map.addLayer(calculate, {min: 20, max: 30, palette: ['blue', 'green', 'red']}, 'LST');
// Select pixel in the image that are greater than 30.8
var mask = calculate.gt(30.8);
Map.addLayer(mask, {}, 'mask');
// Use selected pixels to mask the whole image
var masked = clipped.mask(mask);
Map.addLayer(masked, {min: 10, max: 30, palette: ['blue', 'green', 'red']}, 'LST_masked');

// Image to table example
// Function to find mean of pixels in region of interest
var getRegions = function(image) {
  // Load feature collection
  var urban = ee.FeatureCollection('users/tirthankar25/chicago');
 return image.reduceRegions({
  // Collection to run operation over
    collection: urban,
  // Reducer to use
  reducer: ee.Reducer.mean(),
  // Pixel resolution used for the calculations
  scale: 1000,
 });
};
// Load image
var image = ee.ImageCollection('MODIS/MYD13A1').filterDate('2002-07-08', '2017-07-08')
 .mean().select('NDVI');
print(image);
// Call function
var Final = getRegions(image);
// Export image to Google Drive
Export.table.toDrive({
 collection: Final,
 description: 'NDVI_all',
 fileFormat: 'CSV'
});

// Timelapse example (based on google API example);
var geometry = ee.Geometry.Rectangle([55.1, 25, 55.4, 25.4]);
Map.addLayer(geometry);
var allImages = ee.ImageCollection('LANDSAT/LT05/C01/T1_TOA')
 // Filter row and path such that they cover Dubai
 .filter(ee.Filter.eq('WRS_PATH', 160))
 .filter(ee.Filter.eq('WRS_ROW', 43))
 // Filter cloudy scenes
 .filter(ee.Filter.lt('CLOUD_COVER', 30))
 // Get required years of imagery
 .filterDate('1984-01-01', '2012-12-30')
 // Select 3-band imagery for the video
 .select(['B4', 'B3', 'B2'])
 // Make the data 8 bit
 .map(function(image) {
  return image.multiply(512).uint8();
 });
Export.video.toDrive({
 collection: allImages,
 // Name of file
 description: 'dubaiTimelapse',
 // Quality of video
 dimensions: 720,
 // FPS of video
 framesPerSecond: 8,
 // Region of export
 region: geometry
});