changes pushed for ga guides (#1221)

* hanges pushed

* changes pushed

* changes pushed

Co-authored-by: priyankatuteja <[email protected]>

Author: priyankatuteja

guide/07-working-with-bigdata/01-introduction.ipynb

@@ -16,16 +16,6 @@
     "Geospatial data is a crucial component of big data, adding context that allows for better, multidimensional insights that facilitate smarter decisions, improved business operations and innovation to deliver a strong competitive advantage."
    ]
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-    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
-    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#What-is-Geospatial-big-data?\" data-toc-modified-id=\"What-is-Geospatial-big-data?-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>What is Geospatial big data?</a></span></li><li><span><a href=\"#What-is-GeoAnalytics-Server?\" data-toc-modified-id=\"What-is-GeoAnalytics-Server?-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>What is GeoAnalytics Server?</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#What-you-get-in-ArcGIS-GeoAnalytics\" data-toc-modified-id=\"What-you-get-in-ArcGIS-GeoAnalytics-2.0.1\"><span class=\"toc-item-num\">2.0.1&nbsp;&nbsp;</span>What you get in ArcGIS GeoAnalytics</a></span></li></ul></li></ul></li></ul></div>"
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@@ -88,7 +78,7 @@
    "source": [
     "[ArcGIS GeoAnalytics Server](https://www.esri.com/en-us/arcgis/products/arcgis-geoanalytics-server/overview) is a big data processing and analysis capability of ArcGIS Enterprise. It provides a distributed computing framework that powers a collection of analysis tools for analyzing large volumes of data. Through aggregation, regression, detection, clustering, and so on, you can visualize, understand, and act upon your big data. GeoAnalytics Server allows you to gain insights that may otherwise be hidden in your data, such as patterns, trends, and anomalies.\n",
     "\n",
-    "GeoAnalytics Server works with your vector (points, lines, and polygons) and tabular data and can read directly from CSV files, .txt files, shapefiles, and big data sources such as cloud storage, HDFS, and Hive. GeoAnalytics Server also works with your existing GIS data, using feature layers as input.\n",
+    "GeoAnalytics Server works with your vector (points, lines, and polygons) and tabular data and can read directly from CSV files, .txt files, shapefiles, and big data sources, such as cloud storage, HDFS, and Hive. GeoAnalytics Server also works with your existing GIS data, using feature layers as input.\n",
     "\n",
     "GeoAnalytics Server tools focus on the different spatial analysis approaches you can take with big data: Analyze Patterns, Find Locations, Manage Data, Summarize Data, Use Proximity, and Data Enrichment. Whether you need to complete a quick spatial join, run regression analysis on multiple datasets, or find areas of data clustering, the GeoAnalytics Server toolbox provides many options to explore your data. In addition to the provided tools, you can customize analysis to complete your workflows and analyses through Python, using distributed computation and tools on your GeoAnalytics Server.\n",
     "\n",
@@ -117,23 +107,20 @@
     "\n",
     "GeoAnalytics Server is helpful when you find that your current GIS analyses aren’t processing your data fast enough. It accelerates traditional workflows so you can get results quicker. GeoAnalytics Server is also helpful when you have large datasets and you need to analyze them spatially. GeoAnalytics Server is a good solution for the following:\n",
     "\n",
-    "Your existing tools and workflows aren’t processing your data fast enough.\n",
-    "Your data is growing and you need a better way of managing and analyzing it.\n",
-    "You need to transform your data into something more manageable to use in other GIS analyses (for example using ArcGIS Pro analysis tools).\n",
-    "Your data has a lot of noise and you want to explore it to identify important points.\n",
-    "You want to use spatial statistical analysis and machine learning tools suitable for large datasets.\n",
+    "- Your existing tools and workflows aren’t processing your data fast enough.\n",
+    "- Your data is growing and you need a better way of managing and analyzing it.\n",
+    "- You need to transform your data into something more manageable to use in other GIS analyses (for example using ArcGIS Pro analysis tools).\n",
+    "- Your data has a lot of noise and you want to explore it to identify important points.\n",
+    "- You want to use spatial statistical analysis and machine learning tools suitable for large datasets.\n",
     "\n",
     "**Examples of analysis with ArcGIS GeoAnalytics Server**\n",
     "\n",
     "GeoAnalytics tools are versatile across industries. The following examples illustrate how GeoAnalytics Server can be used with different goals in mind:\n",
     "\n",
-    "- As a crime analyst, you can understand the location and time of crimes in your state, as well as the proximity of crimes to areas of interest such as events, police stations, and city centers. Related tools are Aggregate Points and Join Features.\n",
-    "- As a manager at a state Department of Transportation, you can analyze decades of traffic and crash data to determine the interstates with the most incidents. You can also analyze when certain vehicles were speeding and breaking, and correlate them with the locations of vehicular accidents. Related tools are Find Point Clusters and Reconstruct Tracks.\n",
-    "- As an environmental scientist, you can identify times and locations of high ozone levels across the country in a dataset of millions of static sensor reads. Related tools are Detect Incidents and Create Space Time Cube.\n",
-    "- As an electric utility engineer, you can determine how close lightning strikes were to your electrical lines and substations. Related tools are Create Buffers and Join Features.\n",
-    "- As a water utility technician, you can sort through work orders for leaks, and join them to a dataset of soil types to determine if leaks have occurred in areas where there is particularly corrosive soil. Related tools are Create Space Time Cube and Find Hot Spots.\n",
-    "- As a retail lead, you can experiment with realigning your trade areas based on demographics, past sales, or distance to and from a store. You can also see how store performance is similar and dissimilar across your portfolio. Related tools are Dissolve Boundaries and Find Similar Locations.\n",
-    "- As a city GIS analyst, you can use ArcGIS GeoEvent Server to ingest GPS data on all city vehicles, like public works vehicles and snow plows. See where vehicles have travelled, areas that have with less coverage, and instances where vehicles exceeded the speed limit. Related tools are Reconstruct Tracks, Aggregate Points, and Detect Incidents.\n",
+    "- As a crime analyst, you can understand the location and time of crimes in your state, as well as the proximity of crimes to areas of interest, such as events, police stations, and city centers. Related tools are Aggregate Points and Join Features.\n",
+    "- As a manager at a state Department of Transportation, you can analyze decades of traffic and crash data to determine the interstates with the most incidents. You can also analyze when certain vehicles were speeding and braking and correlate them with the locations of vehicular accidents. Related tools are Find Point Clusters and Reconstruct Tracks.\n",
+    "- As a water utility technician, you can sort through work orders for leaks and join them to a dataset of soil types to determine if leaks have occurred in areas where there is particularly corrosive soil. Related tools are Create Space Time Cube and Find Hot Spots.\n",
+    "- As a city GIS analyst, you can use ArcGIS GeoEvent Server to ingest GPS data on all city vehicles, like public works vehicles and snow plows, and see where the vehicles have travelled, areas that have less coverage, and instances where vehicles exceeded the speed limit. Related tools are Reconstruct Tracks, Aggregate Points, and Detect Incidents.\n",
     "\n",
     "GeoAnalytics Server enables distributed analysis on a single machine or across a set of three machines. With this distributed computing power, your analysis can be performed more quickly and with larger quantities of data than could previously be computed on a desktop machine. Results from your analysis can be stored in ArcGIS Enterprise for use in web maps, apps, and other information products, or you can write back to your own data store.\n",
     "\n",
@@ -158,7 +145,7 @@
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-    "The [`arcgis.geoanalytics`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.toc.html) module available in ArcGIS API for Python provides submodules, data types, classes and functions to process your big data using an [ArcGIS GeoAnalytics Server](http://server.arcgis.com/en/server/latest/get-started/windows/what-is-arcgis-geoanalytics-server-.htm). The ArcGIS GeoAnalytics Server enables distributed analysis across multiple ArcGIS Server machines. You can analyze more data in less time because you harness the compute power of multiple machines.  Navigate to the [Get Started with GeoAnalytics Server](http://enterprise.arcgis.com/en/server/latest/get-started/windows/configure-the-portal-with-arcgis-geoanalytics-server.htm) page to learn everything you need to configure the GeoAnalytics Server with your ArcGIS Enterprise deployment. "
+    "The [`arcgis.geoanalytics`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.toc.html) module available in ArcGIS API for Python provides submodules, data types, classes and functions to process your big data using an [ArcGIS GeoAnalytics Server](http://server.arcgis.com/en/server/latest/get-started/windows/what-is-arcgis-geoanalytics-server-.htm). The ArcGIS GeoAnalytics Server enables distributed analysis across multiple ArcGIS Server machines. You can analyze more data in less time because you harness the compute power of multiple machines.  Navigate to the [Get Started with GeoAnalytics Server](http://enterprise.arcgis.com/en/server/latest/get-started/windows/configure-the-portal-with-arcgis-geoanalytics-server.htm) page to learn how to configure the GeoAnalytics Server with your ArcGIS Enterprise deployment. "
    ]
   },
   {
@@ -167,14 +154,14 @@
    "source": [
     "\n",
     "\n",
-    "The module verifies that your Enterprise has been properly configured with the [`is_supported()`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.toc.html#is-supported) function. Once confirmed the Enterprise is configured, the geoanalytics tools work with [big data file share](http://enterprise.arcgis.com/en/server/10.5/get-started/windows/what-is-a-big-data-file-share.htm) items registered in the Web GIS and also with [feature layers](https://developers.arcgis.com/rest/services-reference/feature-layer.htm).\n",
+    "The module verifies that your Enterprise has been properly configured with the [`is_supported()`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.toc.html#is-supported) function. Once it is confirmed that the Enterprise is configured, the geoanalytics tools work with [big data file share](http://enterprise.arcgis.com/en/server/10.5/get-started/windows/what-is-a-big-data-file-share.htm) items registered in the Web GIS and with [feature layers](https://developers.arcgis.com/rest/services-reference/feature-layer.htm).\n",
     "\n",
     "The `geoanalytics` module presents various tools grouped into submodules:\n",
-    " - the [`arcgis.geoanalytics.analyze_patterns`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.analyze_patterns.html) module provides tools that help you identify, quantify and visualize spatial patterns in your data.\n",
-    " - the [`arcgis.geoanalytics.find_locations`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.find_locations.html) module provides tools that help you identify areas or fetures that meet any number of criteria you specify.\n",
+    " - the [`arcgis.geoanalytics.analyze_patterns`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.analyze_patterns.html) module provides tools that help you identify, quantify, and visualize spatial patterns in your data.\n",
+    " - the [`arcgis.geoanalytics.find_locations`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.find_locations.html) module provides tools that help you identify areas or features that meet any number of criteria you specify.\n",
     " - the [`arcgis.geoanalytics.manage_data`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.manage_data.html) module provides tools for the day-to-day management of geographic and tabular data.\n",
-    " - the [`arcgis.geoanalytics.summarize_data`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.summarize_data.html) module provides tools that output descriptive statistics of features and their attributes based on spatial relationships to other features.\n",
-    " - the [`arcgis.geoanalytics.use_proximity`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.use_proximity.html) module provides tools that define geometries to help analyze what features are near what other features."
+    " - the [`arcgis.geoanalytics.summarize_data`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.summarize_data.html)  module provides tools that output descriptive statistics of features and their attributes based on spatial relationships to other features.\n",
+    " - the [`arcgis.geoanalytics.use_proximity`](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.geoanalytics.use_proximity.html) module provides tools that define geometries to help analyze which features are near other features."
    ]
   },
   {
@@ -188,7 +175,14 @@
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    "source": [
-    "<center>Data that was previously too big or too complex to analyze can now be deeply examined, understood and used to take action. "
+    "<center>Data that was previously too big or too complex to analyze can now be deeply examined, understood, and used to take action. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this guide, we have gained familiarity with how to utilize GeoAnalytics server to analyze big data. In the next guide, we will learn how data can be made accessible to the GeoAnalytics server.\n"
    ]
   }
  ],
@@ -208,20 +202,7 @@
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