add note for geoenrichment licensing and format changes in content pub sample

Author: jyaistMap

guide/05-working-with-the-spatially-enabled-dataframe/part2_data_io_reading_data.ipynb

@@ -2826,6 +2826,13 @@
     "</div>\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note:**  Organizations should review the [data attributions](https://www.esri.com/en-us/legal/terms/data-attributions) and [Master Agreement](https://www.esri.com/content/dam/esrisites/en-us/media/legal/ma-full/ma-full.pdf) to make sure they are in compliance when geoenriching data and making it available to other systems."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 51,

guide/12-enrich-data-with-thematic-information/part1_introduction_to_geoenrichment.ipynb

@@ -20,6 +20,13 @@
     "The GeoEnrichment module provides a python interface for access the demographic data provided through Business Analyst to enrich study areas, access standard geographies, and create reports. Accessing standard geographies enables retrieving and enriching standard jurisdictional areas such as counties, postal (zip) codes or US Census Block Groups in the United States."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note:** Organizations should review the [data attributions](https://www.esri.com/en-us/legal/terms/data-attributions) and [Master Agreement](https://www.esri.com/content/dam/esrisites/en-us/media/legal/ma-full/ma-full.pdf) to make sure they are in compliance when geoenriching data and making it available to other systems."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1492,7 +1499,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1506,7 +1513,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.11"
+   "version": "3.11.0"
   },
   "livereveal": {
    "scroll": true

samples/02_power_users_developers/openstreetmap_exploration.ipynb

@@ -421,6 +421,13 @@
     "In addition to the useful GeoAccessor methods and properties we can access via a Data Frame, we may also pass a Data Frame to the [enrich()](https://developers.arcgis.com/python/api-reference/arcgis.geoenrichment.html#enrich) method to learn something useful about the area we are studying. Even before we fetch data from OpenStreetMap, it should be clear from the differences in population size, population density, and the study area being assessed, that any comparison between DC and Paris would not be fair. At the end of the notebook we will set up a solution to help us find a city that might be more comparable to Paris or DC."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note:** Organizations should review the [data attributions](https://www.esri.com/en-us/legal/terms/data-attributions) and [Master Agreement](https://www.esri.com/content/dam/esrisites/en-us/media/legal/ma-full/ma-full.pdf) to make sure they are in compliance when geoenriching data and making it available to other systems."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -1336,7 +1343,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.0"
   },
   "toc": {
    "base_numbering": 1,
@@ -1365,5 +1372,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

samples/04_gis_analysts_data_scientists/analyze_patterns_in_construction_permits_part2.ipynb

@@ -531,6 +531,13 @@
     "Are there demographic characteristics about the Clarksburg ZIP Code that contributed to its high growth? If so, are there other areas with those characteristics that may experience growth in the future? To answer these questions, you'll use the **Enrich Data** analysis tool. This tool adds demographic attributes of your choice to your data. Specifically, you'll add Tapestry information to each ZIP Code. <a href=\"https://doc.arcgis.com/en/esri-demographics/latest/regional-data/tapestry-segmentation.htm\">Tapestry</a> is a summary of many demographic and socioeconomic variables, including age groups and lifestyle choices. It'll teach you more about the types of people who live in your area of interest and help you better understand the reasons why growth happened where it did.\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note:** Organizations should review the [data attributions](https://www.esri.com/en-us/legal/terms/data-attributions) and [Master Agreement](https://www.esri.com/content/dam/esrisites/en-us/media/legal/ma-full/ma-full.pdf) to make sure they are in compliance when geoenriching data and making it available to other systems."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -909,7 +916,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.0"
   },
   "toc": {
    "base_numbering": 1,

samples/04_gis_analysts_data_scientists/analyzing_growth_factors_of_airbnb_properties_in_new_york_city.ipynb

@@ -1079,6 +1079,13 @@
     "The feature data is now created using selected demographics information for each tracts. This is accomplished accessing the geoenrichment services from Esri, which consists the latest census data. The entire data repository is first visualized, out of which the relevant variables are finalized from a literature study. These selected variables are searched for adding in the feature set."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note:** Organizations should review the [data attributions](https://www.esri.com/en-us/legal/terms/data-attributions) and [Master Agreement](https://www.esri.com/content/dam/esrisites/en-us/media/legal/ma-full/ma-full.pdf) to make sure they are in compliance when geoenriching data and making it available to other systems."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 121,
@@ -10193,9 +10200,7 @@
   {
    "cell_type": "code",
    "execution_count": 190,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -10827,9 +10832,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

samples/04_gis_analysts_data_scientists/analyzing_violent_crime.ipynb

@@ -570,6 +570,13 @@
     "We will add analysis variables, i.e, poverty, to our layer. This will enrich our layer with the poverty data. "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note:** Organizations should review the [data attributions](https://www.esri.com/en-us/legal/terms/data-attributions) and [Master Agreement](https://www.esri.com/content/dam/esrisites/en-us/media/legal/ma-full/ma-full.pdf) to make sure they are in compliance when geoenriching data and making it available to other systems."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 22,
@@ -663,9 +670,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1362,7 +1367,7 @@
    "notebookRuntimeVersion": "9.0"
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1376,9 +1381,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

samples/04_gis_analysts_data_scientists/california_wildfires_2017_thomas_fire_analysis.ipynb

@@ -132,7 +132,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "source": [
     "## Remote Sensing and Image Processing"
@@ -317,9 +320,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -798,6 +799,13 @@
     "### Age Pyramid of affected population"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note:** Organizations should review the [data attributions](https://www.esri.com/en-us/legal/terms/data-attributions) and [Master Agreement](https://www.esri.com/content/dam/esrisites/en-us/media/legal/ma-full/ma-full.pdf) to make sure they are in compliance when geoenriching data and making it available to other systems."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -837,7 +845,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.11.0"
   },
   "toc": {
    "base_numbering": 1,
@@ -854,5 +862,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

samples/04_gis_analysts_data_scientists/designate_bike_routes_for_commuting_professionals.ipynb

@@ -1524,6 +1524,13 @@
     "We will use the Enrich Layer tool to add population data to the clipped layer made in the previous step.  "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note:** Organizations should review the [data attributions](https://www.esri.com/en-us/legal/terms/data-attributions) and [Master Agreement](https://www.esri.com/content/dam/esrisites/en-us/media/legal/ma-full/ma-full.pdf) to make sure they are in compliance when geoenriching data and making it available to other systems."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

samples/04_gis_analysts_data_scientists/drive_time_analysis_for_opioid_epidemic.ipynb

@@ -1166,8 +1166,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The 3 geo-enrichment factors that we will be including in this study are:\n",
-    "\n",
+    "The 3 geo-enrichment factors that we will be including in this study are:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note:** Organizations should review the [data attributions](https://www.esri.com/en-us/legal/terms/data-attributions) and [Master Agreement](https://www.esri.com/content/dam/esrisites/en-us/media/legal/ma-full/ma-full.pdf) to make sure they are in compliance when geoenriching data and making it available to other systems."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "> 1. 2017 Total Population (Analysis Variable - TOTPOP_CY)\n",
     "> 2. 2017 Health Insurance (Analysis Variable - X8002_X)\n",
     "> 3. 2017 Education : Bachelor's Degree (Analysis Variable - BACHDEG_CY)"
@@ -1627,7 +1639,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.10"
+   "version": "3.11.0"
   },
   "toc": {
    "base_numbering": 1,