fix typos

accessing_s3_data.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Accessing ITS_LIVE data via S3 bucket\n",
     "\n",
-    "This notebook will demonstrate how to access cloud-hosted Inter-mission Time Series of Land Ice Velocity and Elevation ([ITS_LIVE](https://its-live.jpl.nasa.gov/#access)) data from AWS S3 buckets. Here you will find examples of how to successfully access cloud-hosted data as well as some common errors and issues you may run into along the way, what they mean, and how to resolve them. \n",
+    "This notebook will demonstrate how to access cloud-hosted Inter-mission Time Series of Land Ice Velocity and Elevation ([ITS_LIVE](https://its-live.jpl.nasa.gov/#access)) data from AWS S3 buckets. Here, you will find examples of how to successfully access cloud-hosted data as well as some common errors and issues you may run into along the way, what they mean, and how to resolve them. \n",
     "\n",
     "## Learning goals\n",
     "\n",

glacier_analysis_grouped.ipynb

@@ -11843,7 +11843,7 @@
    "id": "d5cbacf9",
    "metadata": {},
    "source": [
-    "Before moving forward, we will take a temporal subset of the full dataset to make it a bit easier to work with. We will also compute the mean along the time dimension and calculate the magnitude of velocity usign the velocity component variables. \n",
+    "Before moving forward, we will take a temporal subset of the full dataset to make it a bit easier to work with. We will also compute the mean along the time dimension and calculate the magnitude of velocity using the velocity component variables. \n",
     "\n",
     "Then, merge the rasterized vector and the dataset containing the velocity data into an xarray dataset:"
    ]
@@ -12551,7 +12551,7 @@
    ],
    "source": [
     "#modify colorbar label\n",
-    "cax.set_ylabel('Area (sqare kilometer)')"
+    "cax.set_ylabel('Area (square kilometer)')"
    ]
   },
   {
@@ -12586,7 +12586,7 @@
     "ax.set_xlabel('Median magnitude of velocity (m/y)');\n",
     "\n",
     "cax = fig.get_axes()[1]\n",
-    "cax.set_ylabel('Area (sqare kilometer)');"
+    "cax.set_ylabel('Area (square kilometer)');"
    ]
   },
   {

ind_glacier_analysis.ipynb

@@ -1356,7 +1356,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Both `v_mag_time` (reduced along spatial dimensions, exists along temporal dimension) and `v_mag_space` (reduced along temporal dimension, exsists along spatial dimensions) are positively skewed. We also see that the skewness `v_mag_time` is much greater than `v_mag_space`, suggesting that the distribution of magnitude of velocity is much more positively skewed (characterized by large, positive outliers) in time than in space. Physically speaking, magnitude of velocity variability is characterized by more extreme outliers over time than across the surface of the glacier. \n"
+    "Both `v_mag_time` (reduced along spatial dimensions, exists along temporal dimension) and `v_mag_space` (reduced along temporal dimension, exists along spatial dimensions) are positively skewed. We also see that the skewness `v_mag_time` is much greater than `v_mag_space`, suggesting that the distribution of magnitude of velocity is much more positively skewed (characterized by large, positive outliers) in time than in space. Physically speaking, magnitude of velocity variability is characterized by more extreme outliers over time than across the surface of the glacier. \n"
    ]
   },
   {

ind_glacier_data_inspection.ipynb

@@ -10395,7 +10395,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can see in the above object that while we techincally now have a 'chunked dataset', the entire object is "
+    "You can see in the above object that while we technically now have a 'chunked dataset', the entire object is "
    ]
   },
   {
@@ -10461,7 +10461,7 @@
    "source": [
     "## Incorporating glacier outlines (vector data)\n",
     "\n",
-    "Vector data represent discrete features. They contain geometry data as well as attribute data about the features. For a more in-depth description of vector data, read [this](https://datacarpentry.org/organization-geospatial/02-intro-vector-data.html). We will use vector data to focus our analysis on specific glaciers. The dataset we will be usign is called the Randolph Glacier Inventory (RGI). It is a very important dataset for glaciology research; you can read more about it [here](http://www.glims.org/rgi_user_guide/welcome.html).\n",
+    "Vector data represent discrete features. They contain geometry data as well as attribute data about the features. For a more in-depth description of vector data, read [this](https://datacarpentry.org/organization-geospatial/02-intro-vector-data.html). We will use vector data to focus our analysis on specific glaciers. The dataset we will be using is called the Randolph Glacier Inventory (RGI). It is a very important dataset for glaciology research; you can read more about it [here](http://www.glims.org/rgi_user_guide/welcome.html).\n",
     "\n",
     "### Read in vector data\n",
     "\n",
@@ -12005,7 +12005,7 @@
     "First, we need to use `rio.write_crs()` to assign a CRS to the itslive object. If we don't do that first the `rio.clip()` command will produce an error\n",
     "\n",
     "\n",
-    "*Note*: you can only run write_crs() once, because it switches mapping from being a `data_var` to a `coord` so if you run it again it will produce a key error looking for a var that doesnt' exist"
+    "*Note*: you can only run write_crs() once, because it switches mapping from being a `data_var` to a `coord` so if you run it again it will produce a key error looking for a var that doesn't exist"
    ]
   },
   {
@@ -18451,7 +18451,7 @@
    "source": [
     "## Exploring ITS_LIVE data\n",
     "\n",
-    "ITS_LIVE data cubes come with many variables that carry information about the estimated surface velocities and the satellite images that were used to generate the surface velocity estimates. We won't examine all of this information here but let's look at a litte bit.\n",
+    "ITS_LIVE data cubes come with many variables that carry information about the estimated surface velocities and the satellite images that were used to generate the surface velocity estimates. We won't examine all of this information here but let's look at a little bit.\n",
     "\n",
     "To start with, let's look at the satellite imagery used to generate the velocity data.\n",
     "\n",