fixed typos and added link to gax tool help

Author: jyaistMap

samples/04_gis_analysts_data_scientists/analyze_new_york_city_taxi_data.ipynb

@@ -103,11 +103,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let us access the feature layers using the `layers` property. We can select a specific layer from the laters list and explore its attribute table to understand the structure of our data. In the cell below, we use the feature layer's [`query()`]((https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#arcgis.features.FeatureLayer.query)) method to return the layer attribute information. The [`query()`] method returns a [`FeatureSet`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#featureset) object, which is a collection of individual [`Feature`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#feature) objects.\n",
+    "Let us access the feature layers using the `layers` property. We can select a specific layer from the laters list and explore its attribute table to understand the structure of our data. In the cell below, we use the feature layer's [`query()`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#arcgis.features.FeatureLayer.query) method to return the layer attribute information. The `query()` method returns a [`FeatureSet`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#featureset) object, which is a collection of individual [`Feature`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#feature) objects.\n",
     "\n",
     "You can mine through the `FeatureSet` to inspect each individual `Feature`, read its attribute information and then compose a table of all features and their attributes. However, the `FeatureSet` object provides a much easier and more direct way to get that information. Using the [`df`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#arcgis.features.FeatureSet.df) property of a `FeatureSet`, you can load the attribute information as a [`pandas`](https://pandas.pydata.org/) [`dataframe`](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe) object.\n",
     "\n",
-    "If you installed the ArcGIS API for Python through ArcGIS Pro or with the `conda install` command, you have the api and its dependencies, including the `pandas` package. The [`df`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#arcgis.features.FeatureSet.df) property will return a `dataframe`.  If you [installed without dependences](https://developers.arcgis.com/python/guide/install-and-set-up/#Install-without-Dependencies), you need to install the `pandas` Python package for the `df` property to return a dataframe. If you get an error that pandas cannot be found, you can install it by typing the following in your terminal that is running the jupyter notebook:.\n",
+    "If you installed the ArcGIS API for Python through ArcGIS Pro or with the `conda install` command, you have the api and its dependencies, including the `pandas` package. The [`df`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.features.toc.html#arcgis.features.FeatureSet.df) property will return a `dataframe`.  If you [installed without dependences](https://developers.arcgis.com/python/guide/install-and-set-up/#Install-without-Dependencies), you need to install the `pandas` Python package for the `df` property to return a dataframe. If you get an error that pandas cannot be found, you can install it by typing the following in your terminal that is running the jupyter notebook:\n",
     "\n",
     "    conda install pandas"
    ]
@@ -324,7 +324,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The table above represents the attribute information available from the NYC dataset. Columns provide a wealth of infomation such as pickup and dropoff_locations, fares, tips, tolls, and trip distances which you can analyze to observe many interesting patters. The full data dataset contains over 24 million points. To discern patterns out of it, let us aggregate the points into square blocks of 1 square kilometer."
+    "The table above represents the attribute information available from the NYC dataset. Columns provide a wealth of infomation such as pickup and dropoff_locations, fares, tips, tolls, and trip distances which you can analyze to observe many interesting patterns. The full data dataset contains over 24 million points. To discern patterns out of it, let us aggregate the points into blocks of 1 square kilometer."
    ]
   },
   {
@@ -338,7 +338,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To process data using GeoAnalyitcs Server, you need to have registered the data with your Geoanalytics Server. In this sample the data is in multiple csv files, which have been previously registered as a big data file share.\n",
+    "To process data using GeoAnalytics Server, you need to have registered the data with your Geoanalytics Server. In this sample the data is in multiple csv files, which have been previously registered as a big data file share.\n",
     "\n",
     "Let us connect to an ArcGIS Enterprise."
    ]
@@ -356,7 +356,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Ensure that the Geoanalytics is supported with our GIS."
+    "Ensure that the Geoanalytics [is supported](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.toc.html#arcgis.geoanalytics.is_supported) with our GIS."
    ]
   },
   {
@@ -646,7 +646,7 @@
    "metadata": {},
    "source": [
     "### Aggregate points tool\n",
-    "You access the [`aggregate_points()`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.summarize_data.html#aggregate-points) tool in the [`summarize_data`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.summarize_data.html#) submodule of the [`geoanalytics`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.toc.html) module. In this example, we are using this tool to aggregate the numerous points into 1 Kilometer square blocks. The tool creates a polygon feature layer in which each polygon contains aggregated attribute information from all the points in the input dataset that fall within that polygon. The output feature layer contains only polygons that contain at least one point from the input dataset."
+    "You access the [`aggregate_points()`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.summarize_data.html#aggregate-points) tool in the [`summarize_data`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.summarize_data.html#) submodule of the [`geoanalytics`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.toc.html) module. In this example, we are using this tool to aggregate the numerous points into 1 kilometer square blocks. The tool creates a polygon feature layer in which each polygon contains aggregated attribute information from all the points in the input dataset that fall within that polygon. The output feature layer contains only polygons that contain at least one point from the input dataset. See [Aggregate Points](http://enterprise.arcgis.com/en/server/latest/get-started/windows/geoanalyticstool-aggregatepoints.htm) for details on using this tool."
    ]
   },
   {
@@ -695,7 +695,7 @@
    "source": [
     "Since WGS84 (the coordinate system referred to by wkid 4326) is unprojected, we can use the [`arcgis.env`]() module to set the environment used in the tool processing. The [`process_spatial_reference`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.env.html#arcgis.env.process_spatial_reference) environment setting controls the geometry processing of tools used by the API for Python. We can set this parameter to a projected coordinate system for tool processing:\n",
     "\n",
-    "> **NOTE:** The ouput layer for GeoAnalytics Tools always gets stored in the WGS84 Spatial Reference."
+    "> **NOTE:** The ouput layer for GeoAnalytics Tools always gets stored in the WGS84 Spatial Reference. See the `Usage notes` section of the [`help`](http://enterprise.arcgis.com/en/server/latest/get-started/windows/geoanalyticstool-aggregatepoints.htm) for more information."
    ]
   },
   {