insightslesson2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze patterns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous lesson, you explored your data and learned a little about the spatial and temporal trends of permit activity in Montgomery County. In this lesson, you'll move beyond exploration and run spatial analysis tools to answer specific questions that can't be answered by the data itself. In particular, you want to know why permits spiked in Germantown in 2011 and predict where future permit spikes—and, by extension, future growth—are likely to occur.\n",
    "\n",
    "First, you'll aggregate the points by ZIP Code. You'll enrich each ZIP Code with demographic information and learn more about the demographic conditions that led to such rapid growth in such a short time. Once you determine why growth occurred where and when it did, you'll locate other ZIP Codes with similar demographic characteristics to predict future growth.\n",
    "\n",
    "## Aggregate points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "from arcgis import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "gis = GIS(\"https://deldev.maps.arcgis.com\", \"demo_deldev\", \"DelDevs12\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = gis.content.search('title: Commercial Permits since 2010 owner: rpeake_LearnGIS ', 'Feature layer',\n",
    "                           outside_org=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
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' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://deldev.maps.arcgis.com/home/item.html?id=1923d4e74ac947dab4f8c94d2c2a7a9c' target='_blank'><b>Commercial Permits since 2010</b>\n",
       "                        </a>\n",
       "                        <br/>Commercial building permits issued in Montgomery County, Maryland, since 2010.<img src='https://deldev.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by rpeake_LearnGIS\n",
       "                        <br/>Last Modified: December 09, 2017\n",
       "                        <br/>0 comments, 375 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"Commercial Permits since 2010\" type:Feature Layer Collection owner:rpeake_LearnGIS>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for item in data:\n",
    "    display(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "permits = data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "permit_layer = permits.layers[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "zip_code = gis.content.search('title:ZIP Code Boundaries 2017 owner:esri_dm', 'Feature layer',\n",
    "                           outside_org=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://deldev.maps.arcgis.com/home/item.html?id=78778fe9e4244f71b8194122d1f228ae' target='_blank'>\n",
       "                        <img 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D86Fpf0B9PU2qksxdQ1byH+y2q+dcsM4zhI1PG+Vudq+gALORhR1IpLq9iW7aLcsaJYixt1XO5jks3dnYlnY/7zEnHOBgUPcaVkaA/ePn+GPj6t3/IcfUn0pDJ84pDGKe1Ah4dgcZNAynqkMl5ZXFvHy80EqKCcDcyEDJ7CqWjTJaumkUf7R1Qf8uij/t6H/wAbp2Xf8BXfb8TH8KX0jPdWbOp8qYyrsmWQKJizMmVwPkfcSfV8EAjlyWz/AOBsTF7r+tS1f+LIbK4NsnmTGLJuSgk/cINvzt8uGX5udp4Gc+lJRbV9u3mNys7ff5Avi1RKcgGzWQxfalbzE3bEYbtqMFUlwoYvjOfQ4fL9/YOb7u5ak1u4mleHT4Yrny40bzDKqpmTeF4CncAUOcMPTjrSslvp8h3e0fzNDTbWTT4nE+xp5pXlkKg7QznhV3c4VAq8+lJvsNK25bnvPssLzN92JGchQM4UEnHTnA45FJdh7anKSzX2qXkFzaxNCsMcjK11nZuk2KGVYZTubYXA3YG0nvgi9Ipp/gRq2ml9/wDwDqZ03w+WDjeG/XgH9Kz2LOEl1O70C3NpeRkIluI4J7dWcb1Qoofdna27YFyhUsw4xmtbKTuu+qZldxVn20aOs0u0+w2kNuQA0caq2AAC+BvbjuzZYnuSSck1Dd3ctKysczPLE99fW0tvJdxutuHWNUYL8jEbtzqVPOVI5BGQQQDV9E07bkdWrX2Ho+i24JvIJocoYWNyLptkb/LxIzSxorZxuRxjpkHNHvdLfKwe6t1b1uYlxOBeiS11NJEiMMIxs88xyyqGG5kMc+zO4ugfgHeN3Jrp8Pd+X/AJ66S7Lz/4J1UN3d6ddrZ6gyPHOXEMvCOzKqMFdBhRnLgFSSzD7oBBOdla66dDS7Ts/kWdCkLwyYHyC5udjA5Dq07vuHsGZk7g7dwOCKUtPuX5BH9X+ZnavqUP2i3+zE3NxbzkNDD+8IV1eJ92DtRlzxvZQCDu4ziktHfRWE3qrb3JL7UdQtIHu3hhjiRT+7aVjKc8D5o0aMMSRhQWHq4ySqSWwXa1sWLLTl0+0XzBm5l2tK5OS0hALHPoOQo6KuAOKTf3dCkrLzNlSYYlUffbgexPOfw70iiZVCKFHb/OaQBQAdKAF6GgCRSQc0DOdS0g1a8uJblI54rcrbxLIu4BgoklYKwKhmMioSBnEfBwcm78qSWnUi1279NP8zF1eGVb6O3FvbtbbVjg823jaPfhiYy4bfGMKxBVCFIHyMM1S2vd366kPe1lbpoTaBod1Zus9zIY9iCMQo+4MqF1j85toDGNGAXYFzgMcHIJJrZDimtWdLeX1tYpm6kSJSDgOQC2ByFXq55+6oJOQACSKzSfQttLc5W3vhY3F3dWtnN9nMUTFhGsCkReaZWHmbGYgHgBSxI52ghjpa6Sb1+8zTtdpafcdyWEw8xejfMM+h5H6VmakMsKzo0MgyjqVYZIyrAgjIwRkHsQaNheRzrXF54ehKOn2q2iI2TGQLIEJAVHXadzITgMuAy7SQpzV6S8n2J1j6Eh16a9KnT7WSWNR87yFYlIIJUxMSRJuJDZGQF64LDC5bbuwXvsihealJfQNbvG1ndJPaFVfDgqbuJVlXBXeoYcjjsCRuBppW13Vn+Qm76bO6/MuQabqaoBNfcru+5BHzlvlLF85wOoVUOTjccZKuui/Edn3/A0tN01dODsWMs0zb5ZCqqWbaBwFAwox8qnO3J5OSaTdxpWKniPS5NVsjFBt85ZI3iLHCq6uMseCCAhbIKsPbOKcXyvyFJXWhn6JqNmRFaziOHUIIxAylArfIduEbYqlXKeYqIcY6D5eG091tuJNbddjd1bT01a2a3kCZIIR3jWQxZxlkDYw2OAQRjg9qlPlZTV1Yy7HUY9NkGmXZht2ijQxFSyxuhLgYDj5GGzlTJITyQzYbDav7yEny+69DH0W4u5WltbSSJUhLSxvIhc3YkmlLSEh0IUMApZAwJO4fKUWqaS1f8AwxCvsv8AhzV1CY6vorTxjazReYU6kMnzMnHU7lK9AfUA8VKXLK3mXvE1I5RftGynMZVWB7EEBs/Q8D8qWxW5dQ+axk7dF+nc/j/KkMmpAFAEElykXHVvQdvqaYbCW9wJmwRgjnr/APqo2AvUDMHSSs9xeXUZBSSZI1I6MIokUkHv87OuR/cxVPRJf1qQt2/60NO8sItQVUmDHY+9djujBgCMho2VujEde9SnbYpq+5gGyu9BkiS0826tirL5LNF+6248vbI21gmDtwxdsL/EcYvR76Mizjtquxf0vTjHNJfXag3ErEKBK7rHFtj/AHY3KgB3qWJVF69TSb6LYaXV7lrXHP8AZ10Bgf6NN0/65t360luvVDez9GWbb/Up/uL/ACFIfQlC4pAQXtnHfwvby52SDBwcEdwQexBAI6jI5BHFNO2qBq+hYm4bj0H8hSA5/X9NW/ttyrungZZYiGKMNjq7KrqQyFwu3OQA21uCoIuLs/IiSuvQr2i34t45LSaOeJ0V0+0xsJdjAMoeWNyrMAcFvKyepJOTQ7X1VvQFe2j+8kl1W507a2oRRpCWCtPFIWRNxwpdWRWUbsKW5UZyWHSiyfw/cF2t9u4o8T2UhxEZZI1+9IsMpVScY3fLuG7IwQpGCMnmjlf9MOZdPyOasdLeSxtrjSXXzI418yIt8i3BgaMychvLkiMhd024k6kbsE23ZtS/pXIS0Tj/AE7HQW+qS6dH5WqrIGQsBcLHujlUMQrERbzG7LglWVRnOD2EWv8AD9xadtH947R447mKe8ukWSO7lMg81RhrdMeTuVhgKFG5c+okOGYgD0sl0/PqC6t9fyHW1tLrU0V+hENvAzmBVB82VDldzlgoWKRQrKgViRtbcpAwfDp16gtddktilYara299d2m/ZH5w271ZVE7A+eisflwWCsMldzu+wMNpLadk/wCrdBJpNrz/AB6ixRNo062hP+jTlxC2OYzy7QnjBUruaI/eAVkIICsVvr1/rX/Mfw6dOn+X+RpXtxO90lhZskTLH5sjuu8LGSURVTcu5mYHJyAqr3JAIkrXY3vZGcbvU9IZxMjahEkYk81FWJlJaTcgQFt5ChSNo4BGeuadk9tCbuPn+BftNXluhLHLEYJo3C7d24hXRXUnA4fawDKMgNnDGk1bYpPysXXtRDCWYfPx36ZI49KkYqACSMIADtBY/Ud/w/mKBkOtu7RJaREq95IIdy43KhDNKwz3ESPg9mKmqWmvYT7LroakcSWyLFEoREAVVUYAA6ACpHtoidm8sbR949fYen19fypAQUAPUYoAiurdbuGS3fIWVGRiOoDKVOMgjODxkHntTWmoPXQydMEtvdT2jyvNHFFAyeYIwV3mYMAY0jGMIvUHGOvNU9k9t/0JWja9P1N6pLCgDkjpWq2KsltefaFZSEFyo3x7gSGEyhzIykgBXj2leBtAAq7p7q3oZWa2f3j4tK1H7s2oM6H7223hRiMEYUkOFByCcoxOOCuTSuui/Fjs+/4IraTqcFi0thNcwmK0EaQuzIjFdp+Vju2s0eApKhefvDJptX1S3EnbRvY1rg6drCi1lkimDFXEazY3Y+ZchHBZTwQpyp4ODgVKvHVFaPQr6DJHKbpoSrIbptpUgrjyYehHGPpTlpb0/wAxR627jNR0mYOtzpreXMZ1klDzTrFIoXawMalk3EBBnZ0Xr6ifR7W8ga6x7+ZntqMmtE6SQscx8z7VguAsUc4jIjJQF/OTOG4CqTnnAqrcvvfd936E3v7v3/f+pW1bR0M8dnprvA77mmgV3FsYdrAmWNThPNYLGCuCQWbDFcgT6v5dwa6R08uh0EWrpbkQ3qGyb7qliDA3XASYAJ0AwriN+cBDUW7a/n9xV7aPT8inZW66tNcXcij7LOv2dVHSZELAzPj/AGtyxEEEJk8gqRT92yW619BLW76bEb28s9rNpz83FnseCXgZGS1vJnAAIKNHJx8xRz0ajZ36Pf8AUOnL1W36EOkwr4kJ1K7h8pGSJIPnYSr5TSlnDpsZNzSYG1gWC5IxjI/d91P1Be9q16FY6jcwabaKhkY3bGJpQwaUEltqIZGH7yQAqsjNhME8sVFOyu/LoK7SXmX7HQGm5u0Fvb5yLZHLF2zy9xJyZCcD5N7KR99nHyhN2237/wCRSj30Xb/MqadeW2ntc2Mk3lv57lI5nKpHCrALsMmBgnPAYk8nHFNpuzSEmldX6nSzanb2dmbxWWaJNiFomQgsWWMDcWVBywyWYBRySBUWd7F3SVzntE1NNYvzPcCRJY4/3KBX8qNGAYsWbaGlkVx82xV8vbtyGBNtcqsiE7u7+R3RbYAV4479ep/z0rI1K9AhyigCSmM4ObXrTUroj7TLHaRxoVEKyqZXZn3Euib1VAoXAIySSG4IrTlaW2plzJvfTyL9jqujaduW2JRnO52MVwzuSWOXkdGdzktyzEjJ6Ck1J7/oNOK2/U27TV7O+k8m2lWVwu/C5I2/Ln5sbcjcuVzuGRkCps1voUmnohdWvjptrJcqvmGMDCk7cksFxnBx19KEruw27K5DqGrx2ky2/lzSyeUjkRRl8KSQCcHjlSKEuuxLdtDm9b1e4mspo47WaMSRS5eUbVEQik3Mdu4q+VwqOF3blOQDVxSTWpDejsjoX0SxdFiMEYSNldQFC4ZTuB4wTz1B4IJBBBIqLtF2XYVdGsVXyxbwbM52mJCuT7FcUXfdhZdkc5pujpKly9k8tqPtbGEh5vL2xmNWHkiREdCySR9uBgcKALbta/YhLe2mun/DGxDqV5HEpurObzAo3mJoHUtj5io8/fjOcDBIHc1Nl0f5/wCRV31X5f5mZpGqWQNzeXEiRTtNIrLKQsqRxHase0nOAF3bUyCzHq2abT0S2sSmldsitbyLUb64lgupIAfKiXy1j+bYpYs/nQybQGlKqTsBIbrjirNJaf194rq71t/XoazSXdjHs1BBeW5XDTRR84wMmWD5sqeSWiLjHWNRU27af13Kv0eq8v8AIzNGt5naaXSpwljv2wpKjSpkKGkMXzxMkYZtgUMy7lc8cYb81r16Er+69On/AACC/ttQF5E9zMsMNy0dvJ9nEnzsrSNCCciSLeZXTcrsoO3eSDwK1mkttdRu91fRbaHE3FpYsfsSKqTq81szs3lpGwnHlSs/PyrFvEhIOcrxlcjVX36aP8DNpbeq/E7bR9EtbaY2t95V67BTDLlGQAAuYVjBIiKktIoX5ZEO4Y2EDNt2utC4pXs9f62PQfu8n/P+f8++JuZOoXNlBh7xoU448wrkj0UHk+wAPtVJPoS7Lc5e8TRNUilSGLfK6svmRWczlGIwG+WMDKnB5Zc+o61a5o7/AJmb5Xt+TKuhO+jXDRMt7JDKgc7rd8B1baSifO4RIvKTbvduFGPugN69vvEvddtfuOrn8SacsZlEy4UquznzRlgvMRxIMZyw25AycHFRyvaxpzIqzahc36+VpkbrvIBuZYykcanOXRZCrysP4QEKEnLNt4JZLf7hXb+H7yd9CWOErFLctKEIVpLu5AL44L7JAAC3LbVHfao4FF/T7kPlttf72cxpFlcF1tbq4nkeXdLFc297JIoSPywQUb92QWYAloznzMAcZFtrdJejRCT2bfqmdBbeHDaPIYbqeNZRhgiwBid8sjEsYSBlpW2iJY9o9eMTzeS/EtRts3+BT0vxAyWqxPBdy3ECKkv7pifNCAkMxOctkHJ5IIbvTcdd1YSlpazuD2F9qF5FPNGbWHypV3W8+2RCzK48wbQGLFSGVd67juJ45LpKy19UFm329GZotL/WJ5NOuluBYRygGWQw7yIjKVaNxFGcSMIicK7KNw8wBuXpHVWuTZv3Xe3yOm0jSjpyLJO0st00UaytNIZSCBuKAk7QquzBdvYcknJMN30WxaVvUk18j+zbrgD/AEeb1/55t70o7r1B7P0Zq8fT6f4f/XqSjmfFEZlighRPOEtyitEWMauAkjgOwJ+UMqsRg/dGATxVx0v00M5dF5mQ1uNLi/fw3NvCr4aSK+cwp5kgG8BpUfG5stujHJJORya32t9wvh3TXzL007WEMlzbahHNFDGxMcwSYlgCyhXjeBtzBSqh2fPPpwrX0at6af5j21T+/wDpFeFLnTobaO5gtpjLIsZkMjs5eUtI7ndBxuYuxAY8tgcc09Heza/r1FqrJpf18iz4h0OC6kiuVNt5is7tDdHEVwPLVCWwcq8S7drhXC5wwwRVQeliJpXuc3b3SvNb3Nk72UZsDsgXySrbbsxtCrMhHlg/MHi2vsIYHkCtfXUy9NDWa1srS9sks5khQxXgMsboFDgWozhi8YJxyvOe+TzWau07r5GjsmrFTWtRnjaaF7tUFpbRzxERxFbh2d23PkOAkRiVf3Zjbc3ysGIFUkl0Jk/PYY2oi2hv3ZYoZRY/bIVeOMssztcmTBdSX2uqLtbcBgcc8u21gvuWrqSS1lvUsFjinjezWIrHEpVDBGZQhZCgcqSqFwVBIGAvR27i22LukRW2uw+ZO8txtLKPNlZJMoxSTzIonEPyyBkDIpDbc5Oayn7u2nyNYarX8zorTSrSwO63hjjb+8FG78W+8fxNZXb3NUktkZN/ZTWAP2CeZZryc7I/9H8sSOpd2Znt3cIscbNgFidoVRlqpO+62Xn/AJiats3q/L/I6KHfbQKt1L5jRr88rBUBxyzEKFVR/IDkk5Jj0L2WpTivLK5lURywSTDhdroz9CTjBLdM9O2admu5OnkXjdwKjSGRAkbFWbcu1WBAKsc4ByQMHnJApWKuY/iNvN0yUxNnzFQIVPD73QKmVdPklyEZg4wjk8jg1HRky20NC00u1sjvt4YoX2hCURVJHHBIGSMgdc9KTbe5SSWyL5OKkZgalYSLIdQsQRdqqqVGwCWPehZH34HCqdrKyMOm7GBVp9HsQ11W5Yi1cNOltLBPbvJuCeYI9rFVLEBo5JB0BPWlbqmh36WaNQHYcHoTwfr2P9P85RQ6bhz9aQjmzrOmanG9sZk2yI6MGLR/KfkYZcLzzjjnuOOauzWtiLp6XKGkandC0BFvNcKrSgSmaI7lErgcyShztAC5PXHBIwabSvvYSbtt+X+ZVaG+1aKy1RWimMTJN5UKmNsOoEiB3uHjYqcAhlQ/K2GU/Kz0jeO39egtXaX9fmM1fX7bVLOW1gEnm74o3V0cIjGZBsldAyqDhgcN8wDBTkjIouLuwck1ZF7W9L022ja5miYtIvkKkPUmQFcxx8IZArMVJHYnrSi3svUbSWvyLRtr24sbQukYu7d4pZEZ8BiisCNyoyqzZBIClVOQGIAJWib7BZ2XdFS81dZJPI1TTw6xp5o+WK52joXK7flUkYDDngllUAE0lb4X+gnb7SKV14h0/U3toYYyfJmV+bZnMcSKS2xQjsMlVXKKNuQSQBVLmV7/AJk+67JI34RpOtMwjjhmZDvcNGNwL9WO5A2Tt54zxg84qLyiXaMjO1vTotPg+0WxCvER5MTxRTR+Y2ERY1dS8bM20Bo3XBwSpAqlJvR/5EuKWqJtQNvpdlbJdQRXk6+XGqMEZ2diokaPzFJLZOTgLuJGSuc0k227Oy1G0kldXexRDvPdh9YihgtpgTCjRptduQq3ZbcvnJEoMa8LywU7lKiru1ovXr/wCbK/vKy6f8E6W40q3uERQvkmEYieECN4uvEZUfKvJymCjfxKRWXMzXlXTQ53StKF480OoSy3ZtZ9qiV5QDmMEO0RkZP+WjBCqqCoDADOBbdrculyEr6PWzN1/DlmhUI08flksqR3EyojEEEoofCthmB24GGYdzU8z8vuL5V5/ec5bub+V9HJnkjW7kM7l3bFsEIWJpGJb95IB8qtuKhx90mr297TbT1I393Xf8DqH8P2QjWN4I3RcYbALHaNoLP95jjqWOSeTzWd2jTlW1jm08Nw6TNdX00cU1oqedFGFbzEaMbioB+Qg44yTk44HOb5r2S3I5eW76bma2pvNZ29lA0Zjghs/O+VmYSLPAhQMHVRg4OdrhgCAecirWbfrb7mTfRJdEvzR6ZWJuMORSEKG7UAcc95uure8vriOGKO5uoYoxERuKmSEGSZpGA4XIwiAs2OeCNbaNJdF/mZ31Tbtq/8jsuGHqD+RFZmpy1n9v1PzZ5bnyMSyRrHDHGVURMY8sZUkYsxXd97GCMYzgW7LRL+mZK762LWnaMlpbLb3RW7ZGdgzxoMb2LEKvOBk88kk98YATet1oNKyszXjiSFQkahFHRVAAHfgDgc1JWxiaDs/wBK8vGz7XJjbjH3I84xx97Ocd896qXT0Jj19TZNnDIrK8aFH++CqkNyTyCMMcknnuSfWpKscVd6TBbajDGWltbZ1ItvLlIVbgk71VSpWPehOwLhT8w5LKo0T08+voZtWfZdPU6FtNu0AEN7MMf89I4JB+kcbfm1RddvzKs+j/IraOs8t1cXE7LKFCW6SLGY9xiaUy/KXfIDvsLAgFkIA4yW9EkvX7wW7fyLNyBLqVsv/PKK4l/HMUY/R3pLRP5B1XzLN/Y2VwpmvFQeWvEpOxkAOeJQVZB16MOTQm1ohtLqcLpWpx3Uolupt9tbTO1rEzebcSk/LGfLAMrBRu2lgW3uMnCKTo1bZa9exmn3eienc6RtKbWHlubtDbswjW26GWAIS4k4JCSO5ywVshVCFutTfl0Xz8y7X1enbyJ4QZw2lattmdwSjFQqXEfXKgEhZIj95Ady4WRSQchbe9HT9A/uy/4cgME3hxWezUTWu0s6SzbfKZRkuJGDnY6ghkwcOFK7VZyr+LfcPh22/IWV4r21/tqxGy4twXHHzyeXgvbyBcl1YDA6gEq8Z6Epe6+V7f1qLdcy3/rQ1jqtuLUX7vsgZVfc3Ub8YBAyd2SFIGTu4pWd7FXVr9DlNL1axg1R0tS2y/PzlkddlyuTtzJjhwxyoztcADhgFtp216fkQmk9Ov5nfhmTlT/hWRqL5zd8H6gf4UAZmrwyXtsYoVjEm+JxuLKp8uVJCCQGIyFIyFPXpVLRkvVWRJpl81/5qSxrDLBKYiEkMiE7EfILJGRkPjGOooatsNP8C2cjg1IxBQBzeq2ul6Wr3c0KNLL5m1ed0sjhiVA+bl8nJC8ZzWiu9EQ7R1sU/Bls0KTSywy20srLuV0EaALvKpEpJcqm8gu/LZHJIIDn0SYoK13a34HT2WnxWEO2EscuWcuxZmZuSxJ55xz789c1m3ctK2xZpDM671OK0dYMPLO+CsMQUuQc/MdzIqoMHLMwGeBk8VSXyRLdtOpmaHepaW7QXYFtNDI7OspAAE0srxlXP7twRuGVJGVYdqbWt1/VhJ2Vnp/wSxc6/b42WbLeXDZEcUToxLYONxDYRBj5mYgAdMnAJyvrog5l01ZQu9BlltnlLibUGCsrvny0ZJFlEcS5/doSoXIO44DMSaalZ9kJx08ya81wNYmS2O26kzGkRGZBMMB08vBO+POcEbPulmCNupKOuu36Db003/U2rK1SyhSCIEKg/iOWJJJYsehZmJZiOCScVLd9SkraI55YW1Qyar5k8UcSutuINu94l5ZsOrBzM6jy1YAbVjP8RNX8Pu6edyN/e18rf11MHQJbzWZIv7ZZngcs9uNsapJInLLIExkoAXj3LtYbmB+VQKlaPw/MmN38W3Q9JSKODPlqqbjk7QBknqTjqT69ayNthynFICC+so7+LypMjkMjqcPG4+66H+FlPQ9xlSCpILTsDV9Cjp100hezvwv2mNeVxhJ4zwJlB/gbo6c7X+U/KVJbVtVt+RKfR7/1qYV7JN4ZhaK1h8+3mbECrwYZZDjy2+Vt0bE/uiQSGxExAKEUve30ZL9xWS06eRe0bw4ljDG12fPuI8sCxzHEzHcRCn3Fwf4wN7HJyBhQnLtohqNt9zbubWG8QxTosiHswzg4IyPQ4JwRgjPBqdtirX0ZgXGj/wBmtDc6dHJNIky+Ypm+byPLdCgMrhSqHYVQnkqORywtO+j0+XUm1rOPfv0ETxZDcXJsore5adc5XEK4x94ZeZRx0PPJ6Zo5bK91YXMr2Sd/68yTUbvUprd0sLaaG4ONju1qVGCCcjz36qCo+U8kE46hJJPV6fP/ACG720Wvy/zKWmaiNNV4bpLqS8lPnSL5AyzbEjJQxZiCZUAMzADPzMOtU1fa1vUSdtHe++xqN4r05Ti6Mts4AJ82GROvTOVxk4IGCc4OKnlfQfMuuh0OE9SPqv8A9eoLMXVLedbm2uYE8+O3MhdVZVky6bAVDlUIALEguCTtx3zatZolqzTXQlh1y1dhHIxt5T0jnUxMf93fhX/4AzD3pcr/AOGHzLbb10M+K5vNMmjOozRtb3RZHwoRIJArSIVc4ZoyFKEvznB4ztp2T23X4k6x32/IVp73VFP2LFpC33LiRd8jj+Fo4TgBG6hpCCyHIQZDAso76+X/AAQ1e2i7/wDAI/D0Iia63O8sguGQvI25yiKuwHoAAWbAAAyTgUS6egR0v6kWt2kUd1bXzIJSZFtmRgrArJu2sqsR86Oc5XJCM5xgEhxejW3UTVmn8jp1UINqgADoBwKzLHUDOZ1iys4ZPtrPJb3Mi+WrQH95KRjaojAJlIIX5cEYA3/KOLTe3TzM2ktdn5GZEb7WnOm3itBHC0csku5ElkiO/YhjjaRAzunznft2qfkBwDWkdULV+69DX1NzdFdIsxsDBRMUGBDByMZ6B3AKRrywGX4CVK095/0yn/Kv+GRq3elx3FsLVP3XlgGFl4MTqPkdcY+76dCMqeCaSdncprS33GNZeJLdd9vfypFcQO8bEkBJNhxvQ5I5/iTO5H3KwGBVOL3WxKl0e6LjeItNXpcI3sm529SdqBmIA5JA4HJ4pcr7D5l3N5JYyoeP5wwBVuxB5BAHYjkVGxRzfiGWzkULNKqXcRDwBNrziTI2BIs7nDkAFMbXHUjAZbjfpt17ESt8+nf7jkri71dn/fRCaez6IsZeJXeNHjlYRFma4TLLjZ5IPMbKcsdEorbRP+vu/Ei8vmv6+/8AA7vRJ557GJ7veJ2U7w6CNg248FB0x26ZGGwCcDJ2T02NVtrv9xoOwQFmOFUEk+gHU1IHKtr1xqVv5ulwymPaS8jqFbaWKfuFYlJZFGZeSUwojbMj7V05baN/15kcza91f15FjQNJit7eC4khEV0IVDnLZyVGSwIXDt1fK5DEruYDJUnq10HFWs7anSVBZy+iQu93ezyqHzcFUn6b0UAeSoIyFgZdrMMq755O01o9El5bf13IW7fnv/XY2tQsI9Ri8mQunIZWjYo6MpyrKw6MDyOCPaoTtsU1fQx4r+4029NpfSxvb+Q0yTuFjfKyKpRsMEOAc5VFzkehq7Jq63vsRdp2e1tw1LxLZC2kW1nEs8imOFYjlzI+VTaBjGGIOcjGOOcAii76rQbkktHqJN4Vhv4Qt3JcO5T+OUuI2IBJUNkdRjnII/OjmttYOW+9y/4hjS5tBDKqsjz2qkYA4a5hUjIwRlSRwQcE0o6P5P8AIctvmvzN4ESDaeGHQ/0P9Kgo4jV/smgXiaj8iO3mLOm/Ejq+z51RjhthUEqoBbORlgAdFeS5fuMnaLuWr68trqezktpY5XW525jkVsK8MobIUkc4HJHHbGaEmr37DbWlu509ZmhnalemzjAiAe4lOyGP+857nHREGXduMKDzkjNJfcS3bbfoMsdLS0bzpGM9yww0z/e91QdI48k4RAB/e3HmhvotF2BK3qUdUf8Asmf+0wpaIx+VOqkbuGBhcKSA5Vi6EA78SDaDjFNarl+4T93X7y5oVo8MJuLgYuLpvNkB6ruA2x+uEQKuPUE9zSfZbIqKtr1ZrzTJbxtNIdqRqWY+iqMk/gBS8itjnfDmmQadB9um/dTTxK87M3JIy7Md33T8480/dyBnkZNyd3botjOKtr95qxa1YX7fZ4ZoZC+QI1dWLDBJ4z83yg59s9qmzWtirrZM5K70Fba9gt4RLcWs25mtmlZYoURkDSJiRBhTKuIykhYbgCM5Giejez7mbjZpLbt2/q52VrptrYZNtDFCSACURVJA6ZIAJ/HNZ3fU1SS2Rm6ITcme/wDurdSDYvUhYl8oFuwLFS20ZCjHzE5w3pZdiY9X3NxRioKIbgYjbH90/wAqYMztE/5B9r/17w/+i1pvd+rJWy9EalSUc5qeqzecdM04Brxoy5dv9VCuDgvjcd5OAilcZZWORwbS6vYhv7K3/I1NKtTY2kVu/LquZDuLbpW+aVtzcndIzN268ADgJu7uNaKxo7akohuLKC7ULcRxzBeQJEVwD6gMDine2wWT3MvSo1tnu4gTsjumKgnhRJDDMwHYL5kjkDtmqfT0/VolaXXn+iZqC6hA/wBYn/fS/wCNTYq5ka0STaxA4WS7i3fSMPMB/wB9xLVLr6f8Ah9F5/8ABKviHXW0cKsaB3l4VicBSTgEqBlgOuNy56ZHWiMbhJ8pu6bpUVrnP7y4fJkuGA8yQ8dT2UYARAQqAAAUm7+nYpK3r3K17otpdSiSVP3kZysiM8Tgkdd0bK30yTjt1oTa0Qml/Whzl7ey+H5JVjeSeNbcSqszb9rtMsQ+fHmFQGyVLHOCAVzmrS5vLXp6EN8v3G9Z6ULaU3M0slxOV27pCNqA8t5SKAsYYgZAySFXJJyTDfRaItK2vU1qko5DxlcPb2sYXG0y7nU9GWJJJ9vYjcYgpIOQCevQ6Q3/AK66fqZy0X9dNTr7eTzokkA271VsdcZAOM8dKg1MTxUivpkwcZBCgcsuGZ1VW+UjO0kNtPDYweKqO6Ilsyk2nxaxfSJclytkkCKocgOG3SyCRfussh8sOpHPlLz1FO/KtOtxWu9eliXWb0WOoWrlBJ+4vSuTjayJFJuHBwSqNHkYIDnqMqRK6fqgbs16Mx/D3iRvEWp5MQhWC2mAG7eTvlt85O1R/AMADueTVSjyr5/5kxlzPtZf5HoNZGxhaPiCa7tF4jhnDIOyiaNJWUDsA7OR7NjHHNPo/IhaXXn+epu1JZka5cPa2TyRkqQ0QyMZ2vNGjAbgwBKsRkg4znrVwWpnPRaHOWmtS2unRyBVbYl0wDAD93abVEY8sIoLZwr7fkUAFJDljs4pswUmkOvrucSzDzZBHBHeybVcru8p7chc9uCVBwdoZsDk1VlaxN3e5Qtx/Y0000AzIUnMu4uyyPG0BD7WclSfPAPznhP9r5U4p2Q03HY3IL2aVzl2A32owGIGGv7uNuBgfMiKrccqoByBRZLRCuzS0vUnvndXVVARJF25yFZ5U2tkncR5WdwCg7sbRjJylFRWhvCTbszZrI2Oa1DSrgi6eGdUjuVYvG8O/B8lYiVYSIwJVB13AH+E96TWmm3+ZDT1s9/LyG32iWVvYS4ggLx27/P5SBtyxn5s4yDkZznOe9NN33e4OKS2Wx//2Q==' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://deldev.maps.arcgis.com/home/item.html?id=78778fe9e4244f71b8194122d1f228ae' target='_blank'><b>United States ZIP Code Boundaries 2017</b>\n",
       "                        </a>\n",
       "                        <br/>This layer shows the ZIP Code level boundaries of United States in 2017. The boundaries are optimized to improve Data Enrichment analysis performance.<img src='https://deldev.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by esri_dm\n",
       "                        <br/>Last Modified: June 29, 2018\n",
       "                        <br/>0 comments, 908 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"United States ZIP Code Boundaries 2017\" type:Feature Layer Collection owner:esri_dm>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for item in zip_code:\n",
    "    display(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "zip_item = zip_code[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The USA_ZIP_Code layer is added as a new item.Since the item is a feature layer collection, using the layers property will give us the list of item objects.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USA_Country\n",
      "USA_State\n",
      "USA_County\n",
      "USA_ZipCode\n",
      "USA_Tract\n",
      "USA_BlockGroup\n"
     ]
    }
   ],
   "source": [
    "for lyr in zip_item.layers:\n",
    "    print(lyr.properties.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "zip_code_layer = zip_item.layers[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, you'll use this layer to aggregate permit points.By default, the parameters are set to use the ZIP Codes as the area layer, the permits as the layer to be aggregated, and the layer style to be based on permit count. These parameters are exactly what you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "from arcgis.features.summarize_data import aggregate_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "permit_agg_by_zip = aggregate_points(permit_layer, zip_code_layer, keep_boundaries_with_no_points=False,\n",
    "                                     output_name='zip_code_aggregate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://deldev.maps.arcgis.com/home/item.html?id=5f88962fe9914053b604cabbc8237f1b' target='_blank'>\n",
       "                        <img src='http://static.arcgis.com/images/desktopapp.png' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://deldev.maps.arcgis.com/home/item.html?id=5f88962fe9914053b604cabbc8237f1b' target='_blank'><b>zip_code_aggregate</b>\n",
       "                        </a>\n",
       "                        <br/><img src='https://deldev.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by demo_deldev\n",
       "                        <br/>Last Modified: July 30, 2018\n",
       "                        <br/>0 comments, 1 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"zip_code_aggregate\" type:Feature Layer Collection owner:demo_deldev>"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "permit_agg_by_zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5cdef92094d6495cb50b1c65d3a9ed2e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>MapView</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "MapView(basemaps=['dark-gray', 'dark-gray-vector', 'gray', 'gray-vector', 'hybrid', 'national-geographic', 'oceans', 'osm', 'satellite', 'streets', 'streets-navigation-vector', 'streets-night-vector', 'streets-relief-vector', 'streets-vector', 'terrain', 'topo', 'topo-vector'], center=[38.81819928400006, -76.15872999999993], zoom=9)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "agg_map = gis.map('Maryland', zoomlevel=9)\n",
    "agg_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_map.add_layer(permit_agg_by_zip)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](lesson2img\\img1.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new layer looks like a point layer, but it's actually a polygon layer with a point symbol. Each point represents the number of permits per ZIP Code area. Larger points indicate ZIP Codes with more permits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "from arcgis.features import SpatialDataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "sdf = SpatialDataFrame.from_layer(permit_agg_by_zip.layers[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AnalysisArea</th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>POPULATION</th>\n",
       "      <th>PO_NAME</th>\n",
       "      <th>Point_Count</th>\n",
       "      <th>SQMI</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ZIP_CODE</th>\n",
       "      <th>SHAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.356814</td>\n",
       "      <td>1</td>\n",
       "      <td>14652</td>\n",
       "      <td>Washington</td>\n",
       "      <td>6</td>\n",
       "      <td>2.36</td>\n",
       "      <td>DC</td>\n",
       "      <td>20012</td>\n",
       "      <td>{'rings': [[[-77.0266270001359, 38.98455799965...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.248092</td>\n",
       "      <td>2</td>\n",
       "      <td>48592</td>\n",
       "      <td>Hyattsville</td>\n",
       "      <td>1</td>\n",
       "      <td>6.26</td>\n",
       "      <td>MD</td>\n",
       "      <td>20783</td>\n",
       "      <td>{'rings': [[[-76.9414389999099, 39.02912599996...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.191061</td>\n",
       "      <td>3</td>\n",
       "      <td>219</td>\n",
       "      <td>Glen Echo</td>\n",
       "      <td>1</td>\n",
       "      <td>0.19</td>\n",
       "      <td>MD</td>\n",
       "      <td>20812</td>\n",
       "      <td>{'rings': [[[-77.1384300001444, 38.96841399980...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.168331</td>\n",
       "      <td>4</td>\n",
       "      <td>30017</td>\n",
       "      <td>Bethesda</td>\n",
       "      <td>1145</td>\n",
       "      <td>5.17</td>\n",
       "      <td>MD</td>\n",
       "      <td>20814</td>\n",
       "      <td>{'rings': [[[-77.0943629995527, 39.02250799964...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.360828</td>\n",
       "      <td>5</td>\n",
       "      <td>30001</td>\n",
       "      <td>Chevy Chase</td>\n",
       "      <td>586</td>\n",
       "      <td>5.35</td>\n",
       "      <td>MD</td>\n",
       "      <td>20815</td>\n",
       "      <td>{'rings': [[[-77.0635971995511, 39.01197539974...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.607703</td>\n",
       "      <td>6</td>\n",
       "      <td>16967</td>\n",
       "      <td>Bethesda</td>\n",
       "      <td>154</td>\n",
       "      <td>4.61</td>\n",
       "      <td>MD</td>\n",
       "      <td>20816</td>\n",
       "      <td>{'rings': [[[-77.1429960002652, 38.97162000016...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13.889993</td>\n",
       "      <td>7</td>\n",
       "      <td>38385</td>\n",
       "      <td>Bethesda</td>\n",
       "      <td>732</td>\n",
       "      <td>13.89</td>\n",
       "      <td>MD</td>\n",
       "      <td>20817</td>\n",
       "      <td>{'rings': [[[-77.1267290001432, 39.02947299977...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.978536</td>\n",
       "      <td>8</td>\n",
       "      <td>1383</td>\n",
       "      <td>Cabin John</td>\n",
       "      <td>13</td>\n",
       "      <td>0.98</td>\n",
       "      <td>MD</td>\n",
       "      <td>20818</td>\n",
       "      <td>{'rings': [[[-77.1573989998639, 38.98250600035...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.426954</td>\n",
       "      <td>9</td>\n",
       "      <td>26858</td>\n",
       "      <td>Olney</td>\n",
       "      <td>216</td>\n",
       "      <td>9.43</td>\n",
       "      <td>MD</td>\n",
       "      <td>20832</td>\n",
       "      <td>{'rings': [[[-77.0921479999302, 39.16957599993...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>22.871336</td>\n",
       "      <td>10</td>\n",
       "      <td>8380</td>\n",
       "      <td>Brookeville</td>\n",
       "      <td>40</td>\n",
       "      <td>22.87</td>\n",
       "      <td>MD</td>\n",
       "      <td>20833</td>\n",
       "      <td>{'rings': [[[-77.0616859999089, 39.27760500037...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AnalysisArea  OBJECTID  POPULATION      PO_NAME  Point_Count   SQMI STATE  \\\n",
       "0      2.356814         1       14652   Washington            6   2.36    DC   \n",
       "1      6.248092         2       48592  Hyattsville            1   6.26    MD   \n",
       "2      0.191061         3         219    Glen Echo            1   0.19    MD   \n",
       "3      5.168331         4       30017     Bethesda         1145   5.17    MD   \n",
       "4      5.360828         5       30001  Chevy Chase          586   5.35    MD   \n",
       "5      4.607703         6       16967     Bethesda          154   4.61    MD   \n",
       "6     13.889993         7       38385     Bethesda          732  13.89    MD   \n",
       "7      0.978536         8        1383   Cabin John           13   0.98    MD   \n",
       "8      9.426954         9       26858        Olney          216   9.43    MD   \n",
       "9     22.871336        10        8380  Brookeville           40  22.87    MD   \n",
       "\n",
       "  ZIP_CODE                                              SHAPE  \n",
       "0    20012  {'rings': [[[-77.0266270001359, 38.98455799965...  \n",
       "1    20783  {'rings': [[[-76.9414389999099, 39.02912599996...  \n",
       "2    20812  {'rings': [[[-77.1384300001444, 38.96841399980...  \n",
       "3    20814  {'rings': [[[-77.0943629995527, 39.02250799964...  \n",
       "4    20815  {'rings': [[[-77.0635971995511, 39.01197539974...  \n",
       "5    20816  {'rings': [[[-77.1429960002652, 38.97162000016...  \n",
       "6    20817  {'rings': [[[-77.1267290001432, 39.02947299977...  \n",
       "7    20818  {'rings': [[[-77.1573989998639, 38.98250600035...  \n",
       "8    20832  {'rings': [[[-77.0921479999302, 39.16957599993...  \n",
       "9    20833  {'rings': [[[-77.0616859999089, 39.27760500037...  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "sdf.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>AnalysisArea</th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>POPULATION</th>\n",
       "      <th>PO_NAME</th>\n",
       "      <th>Point_Count</th>\n",
       "      <th>SQMI</th>\n",
       "      <th>STATE</th>\n",
       "      <th>ZIP_CODE</th>\n",
       "      <th>SHAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2.356814</td>\n",
       "      <td>1</td>\n",
       "      <td>14652</td>\n",
       "      <td>Washington</td>\n",
       "      <td>6</td>\n",
       "      <td>2.36</td>\n",
       "      <td>DC</td>\n",
       "      <td>20012</td>\n",
       "      <td>{'rings': [[[-77.0266270001359, 38.98455799965...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>6.248092</td>\n",
       "      <td>2</td>\n",
       "      <td>48592</td>\n",
       "      <td>Hyattsville</td>\n",
       "      <td>1</td>\n",
       "      <td>6.26</td>\n",
       "      <td>MD</td>\n",
       "      <td>20783</td>\n",
       "      <td>{'rings': [[[-76.9414389999099, 39.02912599996...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.191061</td>\n",
       "      <td>3</td>\n",
       "      <td>219</td>\n",
       "      <td>Glen Echo</td>\n",
       "      <td>1</td>\n",
       "      <td>0.19</td>\n",
       "      <td>MD</td>\n",
       "      <td>20812</td>\n",
       "      <td>{'rings': [[[-77.1384300001444, 38.96841399980...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5.168331</td>\n",
       "      <td>4</td>\n",
       "      <td>30017</td>\n",
       "      <td>Bethesda</td>\n",
       "      <td>1145</td>\n",
       "      <td>5.17</td>\n",
       "      <td>MD</td>\n",
       "      <td>20814</td>\n",
       "      <td>{'rings': [[[-77.0943629995527, 39.02250799964...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5.360828</td>\n",
       "      <td>5</td>\n",
       "      <td>30001</td>\n",
       "      <td>Chevy Chase</td>\n",
       "      <td>586</td>\n",
       "      <td>5.35</td>\n",
       "      <td>MD</td>\n",
       "      <td>20815</td>\n",
       "      <td>{'rings': [[[-77.0635971995511, 39.01197539974...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  AnalysisArea  OBJECTID  POPULATION      PO_NAME  Point_Count  SQMI  \\\n",
       "0      0      2.356814         1       14652   Washington            6  2.36   \n",
       "1      1      6.248092         2       48592  Hyattsville            1  6.26   \n",
       "2      2      0.191061         3         219    Glen Echo            1  0.19   \n",
       "3      3      5.168331         4       30017     Bethesda         1145  5.17   \n",
       "4      4      5.360828         5       30001  Chevy Chase          586  5.35   \n",
       "\n",
       "  STATE ZIP_CODE                                              SHAPE  \n",
       "0    DC    20012  {'rings': [[[-77.0266270001359, 38.98455799965...  \n",
       "1    MD    20783  {'rings': [[[-76.9414389999099, 39.02912599996...  \n",
       "2    MD    20812  {'rings': [[[-77.1384300001444, 38.96841399980...  \n",
       "3    MD    20814  {'rings': [[[-77.0943629995527, 39.02250799964...  \n",
       "4    MD    20815  {'rings': [[[-77.0635971995511, 39.01197539974...  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "249.42222222222222"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf.Point_Count.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1145"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf.Point_Count.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf.Point_Count.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52f3b411365041d7a69c96a1bd854a7a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>Dropdown</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "Dropdown(options=('Washington', 'Hyattsville', 'Glen Echo', 'Bethesda', 'Chevy Chase', 'Bethesda', 'Bethesda', 'Cabin John', 'Olney', 'Brookeville', 'Poolesville', 'Beallsville', 'Boyds', 'Dickerson', 'Rockville', 'Rockville', 'Rockville', 'Rockville', 'Potomac', 'Derwood', 'Sandy Spring', 'Ashton', 'Brinklow', 'Burtonsville', 'Spencerville', 'Clarksburg', 'Damascus', 'Germantown', 'Germantown', 'Gaithersburg', 'Gaithersburg', 'Gaithersburg', 'Washington Grove', 'Gaithersburg', 'Montgomery Village', 'Kensington', 'Silver Spring', 'Silver Spring', 'Silver Spring', 'Silver Spring', 'Silver Spring', 'Silver Spring', 'Silver Spring', 'Takoma Park', 'Mount Airy'), value='Washington')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "from ipywidgets import interactive\n",
    "\n",
    "items = list(sdf['PO_NAME'])\n",
    "v = widgets.Dropdown(options=items)\n",
    "display(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](lesson2img/img7.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "v.value\n",
    "x = v.index\n",
    "sdf.iloc[x]['PO_NAME']\n",
    "def f(x):\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "w= interactive(f, x=widgets.IntSlider(min=1,max=1145,step=1,value=sdf.Point_Count.mean(), description=(sdf.iloc[x]['PO_NAME']) + ' ' + str(sdf.iloc[x]['Point_Count'])));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44d16a60dcd1484f970f10522428739b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "interactive(children=(IntSlider(value=272, description='Washington 6', max=1145, min=1), Output()), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](lesson2img/img6.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using ipywidgets you can create a slider that displays the average count(250) approximately on the slider with the maximum count(1145) and the minimum count(1). You can  slide the slider to compare the count of the count of a specific zip code with the average count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_layer = permit_agg_by_zip.layers[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although most of the large point symbols on the map are in the southeast corner, near Washington, D.C., there are a few large points in the northwest. In particular, there is a very large circle in the ZIP Code located in Clarksburg. (If you're using different ZIP Code data, this area may be identified as ZIP Code 20871 instead.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0adab0fb17dd498b9074b02529c21ebe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>Dropdown</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "Dropdown(options=('Washington', 'Hyattsville', 'Glen Echo', 'Bethesda', 'Chevy Chase', 'Bethesda', 'Bethesda', 'Cabin John', 'Olney', 'Brookeville', 'Poolesville', 'Beallsville', 'Boyds', 'Dickerson', 'Rockville', 'Rockville', 'Rockville', 'Rockville', 'Potomac', 'Derwood', 'Sandy Spring', 'Ashton', 'Brinklow', 'Burtonsville', 'Spencerville', 'Clarksburg', 'Damascus', 'Germantown', 'Germantown', 'Gaithersburg', 'Gaithersburg', 'Gaithersburg', 'Washington Grove', 'Gaithersburg', 'Montgomery Village', 'Kensington', 'Silver Spring', 'Silver Spring', 'Silver Spring', 'Silver Spring', 'Silver Spring', 'Silver Spring', 'Silver Spring', 'Takoma Park', 'Mount Airy'), value='Washington')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "from ipywidgets import interactive\n",
    "\n",
    "items = list(sdf['PO_NAME'])\n",
    "v = widgets.Dropdown(options=items)\n",
    "display(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](lesson2img/img8.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "v.value\n",
    "x = v.index\n",
    "sdf.iloc[x]['PO_NAME']\n",
    "def f(x):\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "w= interactive(f, x=widgets.IntSlider(min=1,max=1145,step=1,value=sdf.point_count.mean(), description=(sdf.iloc[x]['PO_NAME']) + ' ' + str(sdf.iloc[x]['Point_Count'])));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd3471c8a0ec4c9595e7cae30ae7a015",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "interactive(children=(IntSlider(value=180, description='Clarksburg 948', max=1145, min=1), Output()), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](lesson2img/img9.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ZIP code has 948 permits.Additionally, this area geographically corresponds to the hot spot you identified in the previous lesson. This ZIP Code is one that you'll focus on when you enrich your layer with demographic data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alterntively you can use see the statistics by clicing the location on the map also."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](lesson2img\\img3.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enrich the data\n",
    "\n",
    "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://www.esri.com/en-us/arcgis/products/tapestry-segmentation/overview\">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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "from arcgis.features.enrich_data import enrich_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "enrich_aggregate = enrich_layer(agg_layer, analysis_variables=[\"AtRisk.TSEGNAME\"],\n",
    "             output_name=\"enriched_aggregate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "enrich_aggregate_map = gis.map('Maryland', zoomlevel=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "671acdb404064287a846f3e17d210cd6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>MapView</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "MapView(basemaps=['dark-gray', 'dark-gray-vector', 'gray', 'gray-vector', 'hybrid', 'national-geographic', 'oceans', 'osm', 'satellite', 'streets', 'streets-navigation-vector', 'streets-night-vector', 'streets-relief-vector', 'streets-vector', 'terrain', 'topo', 'topo-vector'], center=[38.81819928400006, -76.15872999999993], zoom=8)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "enrich_aggregate_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](lesson2img\\img10.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "enrich_aggregate_map.add_layer(enrich_aggregate,  {'renderer':'ClassedSizeRenderer',\n",
    "                                             'filed_name':'ZIP_CODE',\n",
    "                                             'opacity':0.75})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Point to some of the ZIP Codes.\n",
    "\n",
    "Not only has the symbology changed, but now the Tapestry segment is displayed when you point to a ZIP Code. The Tapestry segments have names such as Enterprising Professionals and Savvy Suburbanites. You can look up more information about each segment, including its specific demographic characteristics, on the<a href=\"http://doc.arcgis.com/en/esri-demographics/data/tapestry-segmentation.htm\"> Tapestry Segmentation help page</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " What Tapestry segment is dominant for the Clarksburg ZIP Code where major growth occurred?\n",
    " \n",
    "Point to the Clarksburg ZIP Code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](lesson2img/img5.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the pop-up, Boomburbs is the dominant Tapestry segment for the ZIP Code. Boomburbs have many young professionals with families living in affordable new housing. This description may explain why the area saw such rapid residential growth in 2011. It's possible that other ZIP Codes with similar demographic profiles may experience rapid growth in the near future."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Point to the ZIP Code directly southwest of Clarksburg.\n",
    "\n",
    "This ZIP Code is in Boyds. It also has the Boomburbs Tapestry segment. However, its number of permits has been relatively low since 2010. The county may be able to anticipate a similar spike in permit activity in this area."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](lesson2img/img .PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://deldev.maps.arcgis.com/home/item.html?id=6872cc4c6e9148e096138c3924d396ed' target='_blank'>\n",
       "                        <img src='http://static.arcgis.com/images/desktopapp.png' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://deldev.maps.arcgis.com/home/item.html?id=6872cc4c6e9148e096138c3924d396ed' target='_blank'><b>enrich aggregate</b>\n",
       "                        </a>\n",
       "                        <br/><img src='https://deldev.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by demo_deldev\n",
       "                        <br/>Last Modified: July 09, 2018\n",
       "                        <br/>0 comments, 0 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"enrich aggregate\" type:Feature Layer Collection owner:demo_deldev>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enrich_aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'itemId': '6872cc4c6e9148e096138c3924d396ed', 'notSharedWith': []}"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enrich_aggregate.share(everyone=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `share()` method you can shae your work with your organization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In these lessons, you usedArcGIS python API to explore and analyze permit data for Montgomery County, Maryland. You answered questions about your data's spatial and temporal trends and located areas of the county with rapid growth. You compared your findings with demographic data, came to conclusions about the possible causes of growth, and even predicted an area that may experience similar growth in the future based on shared demographic characteristics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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