{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"}},"nbformat":4,"nbformat_minor":2,"cells":[{"cell_type":"markdown","source":["# Hurricane Florence\n","\n","
\n","\n","\n","```{jupyter-info}\n","{rel-data-download}`gz_2010_us_040_00_5m.json`\n","{rel-data-download}`stormhistory.csv`\n","```"],"metadata":{}},{"cell_type":"markdown","source":["## Overlay - Hurricane Florence\n","The power of geospatial data is it allows you to combine many different types of data as long as you can \"line up\" how they occur in the real world. For example, the `country` dataset below holds the geometry for various states in the United States. The second dataset stored in the `florence` variable is a plain `DataFrame` that stores information about the the hurricane at various points in time. \n","\n","In this example, we will plot the data on top of each other to compare them. As a preview to a future lesson, we will next take this a step further to also highlight which states were hit by hurricane Florence.\n","\n","We start with our imports for this notebook and then loading in the two datasets."],"metadata":{},"attachments":{}},{"cell_type":"code","execution_count":1,"source":["import geopandas as gpd\n","import matplotlib.pyplot as plt\n","import pandas as pd\n","\n","# A special command for Notebooks to get the plots inline\n","%matplotlib inline "],"outputs":[],"metadata":{}},{"cell_type":"code","execution_count":2,"source":["country = gpd.read_file('gz_2010_us_040_00_5m.json')\n","country.head()"],"outputs":[{"output_type":"execute_result","data":{"text/plain":[" GEO_ID STATE NAME LSAD CENSUSAREA \\\n","0 0400000US01 01 Alabama 50645.326 \n","1 0400000US02 02 Alaska 570640.950 \n","2 0400000US04 04 Arizona 113594.084 \n","3 0400000US05 05 Arkansas 52035.477 \n","4 0400000US06 06 California 155779.220 \n","\n"," geometry \n","0 MULTIPOLYGON (((-88.12466 30.28364, -88.08681 ... \n","1 MULTIPOLYGON (((-166.10574 53.98861, -166.0752... \n","2 POLYGON ((-112.53859 37.00067, -112.53454 37.0... \n","3 POLYGON ((-94.04296 33.01922, -94.04304 33.079... \n","4 MULTIPOLYGON (((-122.42144 37.86997, -122.4213... "],"text/html":["
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GEO_IDSTATENAMELSADCENSUSAREAgeometry
00400000US0101Alabama50645.326MULTIPOLYGON (((-88.12466 30.28364, -88.08681 ...
10400000US0202Alaska570640.950MULTIPOLYGON (((-166.10574 53.98861, -166.0752...
20400000US0404Arizona113594.084POLYGON ((-112.53859 37.00067, -112.53454 37.0...
30400000US0505Arkansas52035.477POLYGON ((-94.04296 33.01922, -94.04304 33.079...
40400000US0606California155779.220MULTIPOLYGON (((-122.42144 37.86997, -122.4213...
\n","
"]},"metadata":{},"execution_count":2}],"metadata":{}},{"cell_type":"code","execution_count":3,"source":["country.plot()"],"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":3},{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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"},"metadata":{"needs_background":"light"}}],"metadata":{}},{"cell_type":"markdown","source":["This is a VERY tiny graph because it's trying to show states not in the mainland. It turns out, the visualization won't involve Alaska or Hawaii, so we leave them out of this analysis for clarity.\n","> Note, this comes back to the discussion of how a data analyst has to choose what they include/exclude (e.g., what they deem \"relevant\"), which is where a lot of tricky biases can sneak in. Think carefully about the assumptions you make in an analysis and the impact of potentially excluding some aspect of the data.\n","\n","We start by filtering out the rows for Hawaii/Alaska."],"metadata":{},"attachments":{}},{"cell_type":"code","execution_count":4,"source":["country = country[(country['NAME'] != 'Alaska') & (country['NAME'] != 'Hawaii')]\n","country.plot()"],"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":4},{"output_type":"display_data","data":{"text/plain":["
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9CdWlhZwvGOI9hTcxM71efH4g2yonFllKVPEa0L5d+CvgOm5LT8shLiXiGnlk1LKjPjFtQ+MMphjgTxbawo50DI4/4az0DnsY32Fg6MdiQlDfbENTUQmqCITowK9TkyYMzQik4DBkJzwCLl46xtfICechkOSi5kc5UVvG52mzcgpMrn6jIzu17SYIktnIZHQ9oVwtnuEP9tVy08bW/D6Q1S7pj6l9Xn8eH1BnjnaiU5jRp3Y2bAadQyOBvKiIPOIL8iJC8PoNYFRr2HQCfxByVgwRCgs8flD+ENhwtF5Ek1EJnx1miAsIze5YDjMc8e7ON/npchmTIl4A9z33T3YTXp2f/b6rHn1zHtWIcTtQJeUcq8Q4ppJq74B/D2Rz+3vgS8D74qx//3A/QC1tbULbzFwLEGRywSpKKA7NBrAbTPGnJydDY8vmDO2c6c19wUhFWQipejm6kJ+9v7LeOJgOz/e08KP3r1zxjZGvca7vr+fU13D/NOdm3jpdE9cx+4cHsNmzG1PFH8wzEMvnOF7LzXPGUXrsOgZGp3/qXV9hSNy3FBqs4EO+4I8/KfzvPeq5Sk9brzEozqXA68XQjQDPwGuE0L8t5SyU0oZklKGgW8BM68wQEr5kJSyQUrZUFKSmoKtsSqFZ5Nd9W4azy384aN1YJQBr39ee61BJ9hV787J/Nu5Siol1xeUaRfAf3zDJh564QyffPQg77q8juKCqbnFA6EwX3nmJIfbBhkLhPna705PpGiYj5dO99KepSpF8dA2MMqbvvlHvvT0yXlTIMQzcHLbDBODvrFZ8tAkQ7XLwuqyAm7fErtGQSaYdwQupXwAeAAgOgL/lJTy7UKICillR3SzO4FX09XI6WQyx/N8uG3GGQFFCyEs4WTXMAadwGzQMRYIsaq0AItRj04IguGIqWR3Ux+76t2cj+EPr5hJKu91RzuGqCuyssFhTktmSZNe49G9Lfzw5XNsqHTwxu3VM7bRa4LHD7RN/B1rwn4uqlyWBbczHRxtH+Kd39sT/1Nl1BV3rspLw2NB7NGR+kLy8Y97Hl2/tpTLVhZx7ZpSat3WrHr2LMRw8y9CiK1EBjfNwPtS0aB4GFjAZGGqWV5sS8noezJDo0Ealrk42TVMMCRp7vHgjVFhfXdTH1XO3PwhLnaae70093pZUWLDZTWm9BrwBcP88OVzAFy/thRdDN/UodFg0nmAigtMMY+ZbQ63DvKRH+9LyCTY5/VT5bTg9c98KtdrguUlNjQhJuIsZssEGYt3XlbHi6d7qHJaGPEFuXNbFT0jPj5y3SpCYZmxuZC5SEjApZTPA89H378jDe2Ji3TljkiG831edta5ebVtIKbIJstkQZjruLlkRsmhFBEzSdPndKbbA3hYUWKLvk8t5llMNf/1wsyqVPFQV2TlR+/etZAmpZzeER9ffuYk/7P7fML7uq2GWQd022qdE/lnxvHF+Rs16jVet6WCXctdEIYuj5/bNlXgirqP5soNMC8jMXs9uTFpB9A17GN4LJgTPqGK7HG+18sldS6OdQwzkqIozQ2VDt51ef2M5a+2DfLtPzQldcy3X7qMGreVcFjyh9M9GHUar1lRtNCmLogj7UNJiTfA8pKCWZ9+YiXuincEvqPWhdcXosJuwWE1sNNqmBDvXCIvBbwjxwIRyhwmmrPkPpdLeRlymXR/SoGw5JXmfiqdZgSkJNT+9s2VMSvrLC+xEQwn9rRnMeh4+YHrcFqNtPZ7ufubL0/8jh774GVsq01doFOiXLGymHddXs93X0r8pjQaiP056zVBe4wn9WBYzkh6PG7bFgK21Tj53O3rWV5sw2nNPcGeTt4JuNcfjJnIKpuU2rMn4LmEupVA+8AY22ud7Ds/sOBjjfhi27jbkjAhVjjNFFoM9Hn8fOKnB6cMgh7f35ZVAdc0wfm+5MxPszk0bK1xzjoytxp1eKKTnmaDxtffspXm/jFWlNi4alVJTuQ4iZe8E/CDLYOLKoPfYkJ9LRFSERMAcMO6spjL958fSPg3cLbbw2u/8gIGnTYjjiLbbrndw76k50+klGyqKuRw28UgOqtRx9nu2ZPTFVoNEwK+tcbJa1YWc70pP2MY8k7Af74vdypC5wLKghIfhiRHVbH2mk9rwnLhk9krSwvYWBU7RPv2LRV8/pevMpbgpPnpWTJu7j6bmrS444wFQpzuGiEQCiOEoMhmpNJpmTHx5w+G+fTPD/HzfW2zHGl+zvWNsrN+qidWcYFpTvfa8Yo6dpOe/3jLNqx5Kt6QZwLePezj0b1KwCeTS/otc9gNpSDJsPFketR4bmDBXilXry6ZNTTeatTz1p21c+Y9SYRhXxCPL4jNtDA5aOnz8pc/O8jec/0EppWnKnOYeOdl9ZztHsHrD2HUa7x4umfWup4JMe1LcloNnJ/jnlThNNM17ONXH72CsjxPr5tXAn64bSDbTVDkKWNzBHqkmlq3ZcEuhfMV7f7A1Sv4/h+bU+a6+cczvbx2fWyTTbz4Q2H+NMtovnPIxxefPL6g48/G9BJnZv3cUbKnOkd4284alhXlf1HxvBLw3xxeWApIRXqRgN0cuaSmm3YEM926tOiyqb8/OfHv1K1jF0xeVmSla8RPW/8oJr2GPxRGJwQ6TeCbFHVnzWDuj94R/7zRgfNxonPuAh+lDjO1bivnUjR5nkxlmunUF9l4684afrynJQUtip/QNAEfGJ090E8TkZqkn7hxTbqblRHySsAHciwD4Ti5azjIPJlOh9sXrb60rcbJ4GiA5l7PhKvYzjo3Q2MBzvd5OZrCWqnz4fGHuKTONSOIJBEOtw3S0uedM0+8LYUZ8FaXTU80mjgSslLgwGHWs63GSUhKwlJiN+lZUWIjGJYEQmECIUkwFJ7Ivtg2MLpoysrllYD/1U1reOZoZ7abMYNs2qFzyiMni23ZPy0fTSAsJ2pW7qyLJP6aXmcynYz7hBdaDBzrSLxcnoB50ySkIgvlWy6pwWExsK3WuaDjBENhvvzMSb7xfHIRogvheMdwXIWg+6OpB+wmPf/2zEk+c+u6nImoTJa8EvCeBKq+K7JAjv4W9jT3UeOyUGDSMZLBRGjtA2PYTQbqi60MjQYRIhJ4JQCdLpK7XSdAi7odjk8CSxkJLhkLhLDOMrHYNTRGz8jCBfztly5jQ6UjZtRivBxpH+TD/7OfpgQTaqWCuiJrwjEYf/P6Dbxpx8wEYflIXgl4cUFuRkbl0iBYEZuW/lHWV9g5msRoeCGc6BxmS3UhTT2JiYzdrMc0x2P+Qu3MOk3w8Ht2zeqqGA/NPR5++HIzP3j5XFrrg85FmcOckIAbdRqdQ2N0D/sosZvm3yHHyX46rQRYWVrA6rLsFhFVzEGO38kW6iaXLAdbB1lVmth1OzwW5EcvN8dc92rbIF977tSC2lTpNHPp8uRyoJzr9fDu77/CdV9+nt8e68yaeEMkt4nVqEMfpynEHwrzr0+d4J9+HSnv6w+G6RjMneR4iZJXI3AhBF9/23Y+8uP90SWSUDiiG+MV4WW0jBJIRNQbQUQfVSNPiSJG5XhBsd1I15Avep6L1oCIl4ScsDVHHnNF9OwSSSS155oyO4jYVoRotTIg4vI03h4Q2Ey6iYCMsJQT55Ly4vEBtEnnjLQjsty3gPzGKSdHTSjj7Ds/wIZKR9yFD1LJ6e4RDJogkIDYfesPTdx3Wd0U84aUki8/fWKGn3UimPQa//CGTQnt0+/x87vjXbx0pofH97dN/B70msaaMvvEdTnRVnnx2hUTF8ZFD6PJjiNT8vlEnY2Meo2m7hFG5vHkGQuEJrx9Cow6xoIhwhJ0QmDQCYrsJsIyYqMfHg1MZPY80z3CuV4Py4psdAyOEQhKaovyr7B4Xgk4gNcf4kz3yIIu4Fg0GF0TOYMzSZXTTNsCqqPUqmr2cRMKy5jJoTLBjmUuGhP0SmkbGGXf+QF2RCs09Xn8fPsPZ+kd8bO5Omr6kEwSyot0RM0E4xh1ApNBh0mv8dHrVnH16kh1rFBYzjqRFw5Lfnusk+++1ERjcz/BsGRzdeGUifNEC0kkgttmoMigm7Mqj3fSnMZksQ9LSSAs0XkDE55Ru+rdExPZJy4M8+0/NPGuK+q5MDjGqc5haotSU/Ixk+SdgG+pcXLNmtKc9EZZ8uS4CQWyk3rAZtRxIsnBwX8+f5qt1U5a+0f5v8Mdcaeq3VXvxusL4o+60flDEn8oyDDwpadPcLxzmPdftQJ/MESlyzKjKK/HF+QTjxzgqSPZ+531eQKsr7DPKeAj/rk/j/H6pXVFVu7cVkmvx88yt5V7Lqnhs4+9it2sZ12FI6uV5RdC3gk4RNzCUi3geaA9Mclxq0XOcbxjmE1VDg63pdeMUuk0U+GwoNPgZNdI0tVzhseCfPmZkwnvFwzLiYRN0xkaC/I/u8/zxIF2JPD529dz1eoSfn+yi/87fAGX1UBjc3/MJFeZzpbgC4bnNHvNF7FqN+vx+EMU202UOsz89c1r2VJTyJ/O9vLd+xrYXOPM6RQQ85GXAp4K96lcYSHuW5C/N55sMeILYklhAMxsOC0G9p5Pbam9RIjnqhrPWf5X/3sovY1ZAGe6PVxS56JhmYuzPSP0eS4KtkEXmc/yBSUGnWB9ZSEmvYbZoKPWbaHKGXmV2M1srnZgiyatGguEcFtN6HUaY4EQTx25wOZqJ/XF+Rdan5cC/sKpnmw3IWXk8c1/JnnyOOBJUcWcuchmodvFxLIiK0fbh/D4Q9iMOnbVuznUOshoIMT2Whfn+7zcsbWKN26vmogmvTA4Rp/Hz7IiK2Ep0WsagUkFMMwGHVesKp74+46tVRnvV6rISwF32/I3/eN0Fqp5LquBUvvMZPwT94WoN4DDYmB4NDhprZjk4zJ56dQ8JEKIKW2cvE5O/WfqeefhfJ83a4FZnUPpr+iUqqIjeXJPTAtmg4bXF5owBXn8IXY39eG2GjAbNG7aUMa6CgeVTgu/O97FH0/30NTj4ef726h2WfmHOzawvKQAu1mHhcUROj+dvBPwtoFRXmlK/aNp1n4oCzzxwdbB+TcCGpa5svpIP52I90x2BDwTbss1Liv93vi+m7lYTA9oieIwGxgLzrTjF9tNBEOS/3z+zJRBgNWo49q1pfzrm7Zww7rSJfEUlHcC/q9PHscfyiHf5wWiCjJknkAozJoyO20DXsJhSTAsCYUl0z1TNRGJWNRrkacQTQiMeg0hIsv0Og29BjpNw6ATnO4awajXsaHKweHWgZS0NZcuj1K7KaPtqSu2sWda/pr6IivrKxw8daQTs0Fja42TZUVWLl1exOu3VGYtWCtbxN1bIYQOaATapJS3CyHcwE+BOqAZuEdKmdYhXr/HzxOHOtJ5ioyTqR/EdHPJUmZ4LIi5WJs3L0pYQjgkp8YczBFYsq3GyVggmLC/91zk0rfmMGdWHPc09VHrtnC+76I3zK7lRXzgmhU8eNfmrPn05xKJPGN8DDg26e9PA89KKVcBz0b/TiuFFgMFi+wOq+XUGGvpcPzCMDXuubP9Jcr+lgFs5tyYn0mH8GcjYt4flLhtF3Mg/eSVFv7yZ4eUeEeJS8CFENXAbcC3Jy2+A/hB9P0PgDektGUx0DTBl+7egtOaGz+SVJBLI6xMku3bli8YxuOLeDakkrNdKY5MTPYCScOFVVRgzPiT3IWhMeqmhbhvWkACrsVGvMPZfwf+Cpic9b1MStkBIKXsEEKUprhtMXnt+jK++fYdvP3buwnmVDJsRSIsK7ZSXhipRxgrFHx6RZ7J37SYts1s28ZzkzDoBLub+lKWmqHaZaHPm7rJ2WQFM5wGoW0fGMuoB5jFoGNtuZ195wemLN9a48xYG3KdeQVcCHE70CWl3CuEuCbREwgh7gfuB6itTU2ugUuXF/Gpm9bw4G9SV2MvW7eCpTqJ2dzjnbNyeCZZXmyjuMA0UQBiIRgNqfV8EEk+q6Q6vmBtuZ3jF4bTLuAFJj3rKx1oIhJ0Nb1QB5CXATfpIp4R+OXA64UQtwJmwCGE+G+gUwhRER19VwBdsXaWUj4EPATQ0NCQssvqnoYavvjk8cUVCLOEyKUb19keDzZTakwpR9uHJsQuFeTK5LMjWo5sYDTAmmhK5/GWlTvMdAyNTfwWzfpIhGOJ3YxeJxjw+BkLhtDE1JubnBY/IIAyuwmvP8T+tsGYT0UrSwtYW77w8m+LhXkFXEr5APAAQHQE/ikp5duFEP8K3Ac8GP3/F+lr5kwsBh1GnZa6dKq58TtZMuSQflPuMHE6RbZrrz/EuT4va8sLOH5hJCXHTI7UXdA7690T7nwtfTPzozgsBk51jlBqN7GsyErPiI8im4kDLf2MBsLYjDqsRj3dc6TAsJv0XLm6mJ4RP7VuK3unmU3G+eJdm5eEf3e8LOSTeBB4rRDiFPDa6N8Zo3NoLKW5sHNlpLNUyKUR+IUhX0oLhYz6QzT1eGmomxkhmyjJVrZf6PSQQSfYVutkU1XhDF/s6QhgXYUdfyjMK839NPV4aTzXz/qKQrbXOvH4Q6wtt+O2xq6otbbczpsvqeFQ6yDhsOT3p3r47G3rsE6aYLYadfzLmzZPpNZVREhIwKWUz0spb4++75VSXi+lXBX9P3MVY4nkSFhRkv+2sIUms4qbnLs/5ZCCE4lo3VnvxpUiDydfMExjcz/VruRdFa0GLemQfLM+8tNOtmivy2pk//kBDrfNH03a0j/KsY7hKRkXjXqNw20D7Ds/wCV1LloHRicmrSfjthn50t1bePpoJ639owTDYfo8fsJhyc/efxmfvXUdRr3Gt+5t4J6GmqT6spjJ22cRIQRXripJ2fH0Wt5+FHGSW4KZW62JsKepjzLHTJFZCE6rgYIkXRW9gXBM0ZsPATT3eigpMLGxypHQvhurHNQX2xKqeH9hcGpuGYNOsK7cjj9qw46Myj2c6orUBy13mCmM2tT7PH7OdI9MtNMffare3dTH+koHV60u4cb1ZVy2Irnyb4udvFatnfXuuGvhzcfec30peeTNVdLhVrYQcsmEkk7GAmGMBo3lJbZZTQhzkUzMgwSKCkx0j/g42DIYt9ud22rgxIVhgkmmqtBrglq3lWqXNWaOnkBIcjCaSXBdecRkVeO2UOu20hct2jAaLXn23Ikunj/RRa3Lwlffsi1zT6p5Rl4L+K2bKvjG23ekRAxCEhqb+7mkzpX0Y2cuk2veOrn6CYdT/EGd7orksPYHw0n5hyeipdtrneysc2E36znWcdEL5kDLAEadYFe9m601Ebu2QSe4pM7FmkkeHaUOM4GQpKU/uSK/22qdnO/z0jRPmbUqp2ViknJjZSFOqxFDdGLSG62wIyW883uv0D40hrYIf4+pIq8FHCKBPX/3+g2YU+R/+0pzP+vK7RkL2c/YpZljAp6rpOtGV+lM3Ba+rsLO3nPx5VUx6QVtA6Psae6fqAE5GX9IsrupjwMtEbu2lJJXmvsJhsKsLI2MhpN5mt1cXcjm6sgN4Uz3/J48dUVWhscCEy6CTT0erEbdxETpyLS2dydgylmK5L2AA7zjNXV84XUbUna8V9uHcFoNVCRhf8xZcmwQk6v3k3S0y6jXONudmEuhUSfwzJNsazJum4nOofjFbtyB60y3hwKjjmVFVl6dpWzZbAgReWI50jZIid00YQaZC4fZQEOdm9dtrgAiOWleOt2D2aCjstA8kftbr0UyPxYlYXZaSiwKAQd4y87aKQ7+Bt3CFKu1fxSvPzjlETMdZErIcs0stNRsmolej2vLHXNGqhZFEzyVFJgoshnpGEy+SIVOJzjXm7i3y45aF6+2DRGSkTD7eDjUNshj+9sISckXXreemzaUcbbbw5Mfu5L7r14+8eQbDEvWVzhYpYJ25mTRCHg4LCcma25YV8oDt6xb8DEHR4Oc7Rphe61zwceajUzJWI7p95LCHwwTChN3BKFRJzgVY8Su1yLrVpUVIARsrIx4jMxVtT0eWvpGsSdoMjToxIKqDv368AW+8MRRfMEwt2wsp8xhZktVIYUWA5VOMxurHLz78vqkj79UWDQCrmmCB+/azLfubaDGbY3LfzUeAmHJvvMD7Kp3p+R408lVU8JSJV0VyruGfZzsHGZXvRv7PGH7lU4La8vsXFLnYm25nYZlLmpcFioKLawut2PUafSM+Hm1fSgl+Vu6hn2sq4zf3VCnCVaX2elcoH26otDM8ye6ec8PG+n3+il3mvnG27fTO+Lnkjo3t2+pWNDxlwKLK7k2kUnNjsFRCkx6/nCqO2V1F3c39bFjmYuDLQMpzYK4VEfGudrtdCa4DMvIdbSixIbRG5h15FxqN3G4fYjRSVGYRr2GPxhO2kNkPl5p6qPGbYkZKj+dbTVOGuOcXJ2L4bEg16wpod/jp6jAhJSSDz28n2vXlPI3KZzTWswsmhH4ZIIhya8OdXDZiuL5N06Avef6WVNuT2kO6SVmCs55MvF1nOn2RGuCztIGIaaIN1wMcEkXEugf8cf1pJkqt74RX5CDLQOsryxESslLp3vZd36AmzaWpeT4S4FFNwIHeFNDNQ8+eTwtF/2R9iHqiqxYfMGUjO5zzT97qZOpryPWjXt9hQOQHExRPc1EqXRZONfnxW0z0ufxI8TF63NTVSFmg0b7wOi8uVESod8b4GjHEN0jPv54podSu4mbNpSn7PiLnUUp4A6zAYdZnzLzyXSae72U2k3Uuq05k9M678jZJ4/MSPh4Rr1ql4VSu4n95wewmnQpracZL26bkZUlNva3DBAIScrsJnbUugiEw4SlxOMLcfzCUMqKXoyzsrSAUX+Id11eR0mBiZb+UT5wzQqsxkUpS2lh0X5SX3/bdt7x3T1pe/TsGvZhN+lYU27nxAJyP0vJrCaZya520/Vu/KekiYvb+YPhiyVqJu04XlHdOinYaTz/cix3Pn8oTCAkJ+zzmphaWEAikfKivXj8EDoROZkmIucbP34oHAYEwXAYKaPnlqATkX0FIvJ/dD9NRP8e74qMHSEpo5+fRE4cYy5ifDRTjjX1TXo53jHEyhIrvmBkkrzUbsrKjLbLasAfDLNn0o2jc9i34AnKeDjbPcIHr1nJV545yeu3VPL529dPqX+pmB+Rrln3WDQ0NMjGxsaMne9cr4eeER9v/dbutAm5QSdYX+ngYEtyXi/LS2ycjSOCbaFcUufilSyM7gC2VBfGzI2Ri9QVWWlOwic6WTZVF3I4S59NldOM2aCLK4IyXTitBm5cX0YgJLl9cwXXr1P271gIIfZKKRumL1+0I3CAZUU2lhXZqHJa5s3PkCyBkORgyyANda6kHn9z1pKQQvLJzJ/JACODTnC+d+Z1OR7zk2KLxQQuq4GVpQXsOz9AKMt1ZQe8AR5pbAVgcDSgBDxBFrWAA/zw5ea0ifdkGpv72VXvZneCEzxqEnNpsrHSgcWoi/lUtKzIRvvgKDUOM6GwJEzEPz0UlgRDcsJUZDPpMRt06DWBThORbaXEqNMw6LQJ89TkUYJOMOEGu6N2PPvmpLLQQuIPSAqt+khmwKj5atyEFQxLgqHwFHdLGd19uklq3AQ32bwlozYxETWVwUUTXJ/Hz/BYALs5c4WT851FL+DBdA1jYrC7qS9hU0Wqs9/NRjbvExcGx2YN5Z9vvLu5unBGVfJ0YjXqptjmJ1dqilVgeHolp4kR/PQPXEBdkY1TXZEIS50mZlwn43MA5/u8BMNyXlNOuibp9ZqgwmmOyyc8lbxhayU9I34l4AmwKP3AJ3PzxnJWlaauXNZ8vNLcz7ZaZ9yZ3TL1yJ5NU42UEArLmK/gPK9M33gsBh0hGUn2FAhLgmEmXoGwnPGavD4YjpjUAqGZ2+mEmPCf3lDpiFn0ePxzSmWgWKLYjDrcNmNGxHs8n4sQUOu2cqprhJ456mYqZrLoR+CVTguPfehybvrKC7QNZGZEsf/8AJuqHJzsHIlZt3NXvZtej59CiwGdRkZMPNkkn+qNpiv39Koy+0R6B7NBl9J6rgul3GHGbTMQCks0TUzJJZ5O3DYjY4EQ73hNHVuqC7lmTSm/P9mFLxjCpE9dsNxiZtELOECBSc933tnALf/xh4zZnA+3DbGmrIDW/tGJFJnjhKTkdFc2K5ZnlnzKPJgub6ULg2O4bUZkWOILJFeoOB3sWOaitc/L+V4vI0kWUE6WC4NjvHF7NWe7R3jm6AVOdo5gNek40jbI9mXpyT202FgSAg6R9JyfuGE1X37mZMbOeaJzhPpiG30eP4OjFwu+6vJI0FJBJl1VF8pAElVz4qE7ahrYWeeKaT7JNKvLCrCbDXEXjEgHw74gP/rTuYm/D7T00z3i44a1ZYSjTwOKuZnXBi6EMAsh9gghDgohjggh/ja6/AtCiDYhxIHo69b0N3dhvPeq5bznisymqGzq8eAw6ykuuBigEEiy5uBCyCMNzSqJFEVIFLtZz8BogKEYFXMyQXGBkfUVkSyHTT2erIr3OCb9RQk60DLAjevLqS2yLjhF7lIhnhG4D7hOSjkihDAALwohfhNd9xUp5ZfS17zUYjboeODWdRQVmPjik8czdt6W/lEqCk2UO8xcGBrLiv1ziQ36k2Y0EMJtNdDnDcy/cQLoNVhTZmff+eyI5tYaJ4dbB9LmuZIoa8vtFBeYqC2y8tNXWgiFJV94/Qbu2FrFWCC04IIsS4V5BVxGnn/HDbaG6Ctvx3M6TXDntqqMCjhAx6CPkgITVS4LR9qHMOkFvmDmPkY1Ao+f8QK7qSQYhqGxQFrT1c7GpqpChscCaQsMSobjF4YRYpixgIv9n38tQ6MBql1W/MEwxy8MTxRnUcxNXFeqEEInhDgAdAHPSCl3R1d9WAhxSAjxXSGEa/Yj5BblhWae/eTVU8wamaB7xMeoL8jacntaRGIusjkCz7ebRzpMXIUW/YwUsZnAbtJzuG1w1nD5+mIbO+vdlDvMmPQa9qi5r9plYUWJjbXldnbVuyl3mFLaLp0mkBJ21rsxaBoVhRbO93kYGPUr8U6AuCYxpZQhYKsQwgk8JoTYCHwD+Hsio/G/B74MvGv6vkKI+4H7AWpra1PT6hRQX2TDF8i8KaPPG8BlMzKSQMHafCffzDf+FAu4JmBLjZOeYR8bKu1IOfUzuXiDiyy0GXX0evxoGiAjro2aEJHYgvEkX3JqIjCn1cDQaCAatXnx2OMJvMb/l9GoSotBR3Ovl6YezxQ3Vl8wTKwp1hUlNiLW1NSwusxOfbGVV5r7+ODDe/mb121gWZE1rzyWcoGEvFCklANCiOeBmyfbvoUQ3wJ+Ncs+DwEPQSSZVfJNTS2aJlhWbOXVtsQqcaeCc71eVpTYMppEKGc++Dwgla6EAthe62L32b645z521rs5m2BswM56N/vjTKhmMWjYTImlW27tH52oDauJizcSTQheae5L2DR0rGOIYx1DlDvM/Nubt1BXbEvsAAogDgEXQpQAgah4W4AbgC8KISqklB3Rze4EXk1jO9NCOEuxFMFoFGImyXYkZrKEwhKdSF9ip1j4U3iyEruJ9sHRhCaug0k8AYQSaHMwLKkrsiUk4L5geNaUBuMTpPE0Qa8J/vzyOm5YV0aN20rPiI+NlYVxt0MxlXhG4BXAD4QQOiI280eklL8SQvxICLGVyOCuGXhf2lqZJr51XwPfeuEshRYDTT2eCQ+B1jTVHZxMc6+XjZUOXm3PzBNAvtmhxznUOsi2WicnOoZw2oyM+IIMjabPDW88KVSqMOi0hCOAvf7E+9frid+8EQhJGs/1s62mkKGx4IKfBA+0DLCtxsmBloE5n/R2LHPxD2/YyLqKiwWUK52WBZ17qbOo84Eng5SSR/e28rnHX017HUK9JlhWZM2IKSWb+cDHS3QtBJNeIxiWbKoq5EDLQGoaFgOzQWMsRXMjK0ttDHgDCbvuVRSa6RgcS2gfvQaSxG8+G6scKTMjbq91sv98bBG/fm0p33j7Doz6RZ9+KS3Mlg98yX+a4egFHwiFCYUjaS7vaajhL25YjcOcvkBVs0FjW60zq8n08wlfMPL9HG4bpMhmZHutk131blaU2NhZ56bGnZqRnEFLzU9CE2DUaUn5XSeT0CkYhspCc8L7HWkbor549gLLibDv/ADbonbyydiMOr58zxYl3mlgyYTSx0JKSdvAKE8ducCWGifPHe/i1k0VbKwq5APXrOCdl9Vx7MIQDrOej/74AEc7Umfu2FhVmLURceZJ3VNeKCzp9finROqd6fawttxOQbTE3eHWwUi2QxmpOtMxOBZzks1u1uPxBVlWZJvwxNCnKIBkU1VhUpWWCi2GKWkXEqGowEhLguY/ScRsZDHoGE1BjpZ95wfYsczF3nP90dS8gps3luO0qlJp6WBJC7gQghq3lXdfUY8QgkvqpibQsRh1bI8mvb91UzlNPZ6UXOSQ+UnFbNrAM5E54PiFYeqLbew9N0C1y4JJr+GwGGjp87K6zE6BSc+JzmHWVzgYHA3Q7/Uz6g9R6jBzrtfDrno3+871TxQbXih6ncawL3FbtttmTFrAk83gd7rLQ43bQjhMSjJ2VjktPHDLWrbXuhAifXnLFUtcwMeJx/f0fVev4K4d1bxwspu/feIo3gUGZeTrpGIyJONVkQzjo+jxSWijXsMfDE8IiMtqmFExaTwvye6mPkrtJlaVFjDg9SNI3hulxm2hYzA5ISwwJf+TXEgAUkvfKJuqHHSP+BY09/PJ167mI9evmrKsxJ7aICDFRZRRKg66h30MjQaoKLTw5ktq+dVHrliQzdVu0nMug4Vzs00oS3er6ULUP09+k65hHy+d6cVi0KHTtEil+CRwWgwMJ+kpY1qAnXi+/s3H4bYhalwWLIbkRvLXrS3lw9etXFAbFImhBHwafR7/jBFjid3Erw51TEx4Li8p4PEPXs7acntS51hXaZ9IL7oUyHbh3EQZGgsyGgiRbDbT5l5v0hN2C0mh2tbvXXDU65luD3VJTmq+98rlKpIywygBn4ZJr8UMurjvsropP66iAhNffeu2uEunLWUCuZRFKQMYNIHDklxdx4W49fpDkjJH4p4o0+kZ9if89FHjtiQ9oFEkj7KBT8MWwwZ5rtdDtcs6ozDv6jI7ly4v4sXTPQmd43zf6MRMvSJ3WVlip8Y9PhodzyYys9L79Fu4QS841ZlcxaWF5ucpKTBxIUEf8ul0j/gw6gTLS2zzetLoNcGnb1nLtWtLcdmUp0mmUQIeB+WFZl463cNVq0tirkuUC4Nj1LpS43ub61yUvfzjdPcwF5Io8FBkMybtjuhJIgpzMlZjampJ+kOSYptpTgHXBHzw2pW887K6lHnvKBJDCXgcmPS6mOINcOP6Mn62t3XKsgKTnlK7iWK7iZICE0UFRooLTLhtRtxWIz/60zkaz/XFPJ4i/xnxBah0Wkkme1+yLoTjtKewcPeIb/a2mPQa/3jnJt60ozpl51MkjhLwBXLjhnK+8WfbKSowUeYwUWI3YTXO/rGO+kN86ekTS84uvJTwBSUuq4GmBPcTggWnHGjpj7gDHk5BePzRjmF21bunuF6uKi3glk0VvOfKehzm5Oz8itShBDwF3LKpIu5tLUYdz37yag62DvLYvlZ+ebB9we5fityja9iH3aRPKJjHZTXQ51n4tWCZYwCRKIdbB3BaDQxEr9HLVxbzideuTtnxFQtDCXgWEEKwtcbJ1honn71tPc+d6OLRxlaeP9FFMF0ud1lyllmKzxmFlogJLdGslk6rMSUCnkq8gTDFdjPDY0EefOMm7m6oyXaTFJNQAp5ljHqNmzaUc9OGcnpGfDy+v41HG1s50RmrLsoCSFBJHRY9K4oL4pyFnFRiZsINTkz8t3+WPNK5jjnJgBaAgSRs2QuJwpzM8RTm7AHo9/p54Ja1SrxzECXgOURxgYn3XLmcd19Rz7GOYR7b38rjB9rpHs580I/TYuTYhaGUpVbNR4SIpGlNJLLcYtBYWWpPykU0FXVS15bbOX4htTf/dRUO7n1NXUqPqUgNSsBzECEE6ysdrK9cz1/fvJY/nOrhZ/taeeZIZ8rqNd64voyH7p2RXngKLX1e7vvenqSy6i0Gmnq8bKh0cCSBohurSu0caY+vtNl0wikwnyVS+ScejHqNz922TqWCzVHUt5Lj6HUa164t5etv286ez17P392xgY1Vjvl3nEYy0lDjtvKXN65JYs/Fgy1Bv2pfMJz0U0swBTX+mno8uG2p8w6pdllYW5749abIDGoEnkc4rUbufU0d976mjqPtQzzS2MLhtsGJnB1SRorMSmQkWjCKRFLtiiTfEkRMA6vKCuI65y2bKnj4Pbv4zGOHl1QCrnGOtA+hCeIq2uuyGuj3Ju8GmArXUrNeS+lE6O2bK9XoO4dRAp6nrK908IXXb8jIuS5fWcxP7r+UO772El1ZsMdnE48/FHc5uuUltlkL/8bDQtLBjlNoMTCWwu/oshVFKTuWIvUoAVfERUWhhWc+cTU3//sLCddrzHfizY89FgizzG2NZuSbPJqOPg9FU6i09seuUh/rPNUuCzohECLyhBWWcsLRR47nZomuj2wn2OQwpSSQ57ZNFVy6XAl4LqMEXBE3hRYDP7n/Ut7xnT2c71s65pTpScxmI57JTvMc5ojpoq4T0OuJVA5KhB3LXAltP5mI540gEJL4gqmpPqVIH8q4pUiIZUU2nvvUNUvKMyGYwrQHa8sds3qKjEXL9Rl1GnaTnpCE9eX2hFMWn+4apqHOxbYaJxurHKwuK6DWbaXEbpq3WEPDMhd2k55L6lxsqXEmdF5F5pl3BC6EMAMvAKbo9j+TUv6NEMIN/BSoA5qBe6SUKj/qEkCnCd5z5XICIckXnzye7eaklVq3lUNtybkFxqJ7ZAyDTsScsBwX9ts2V1DmMPPN35/hSPsQBp1GMBz/aHhwNEjjHDZ7nYg8TdlMeqxGPSaDhkGnEQpLwhL6vAH6mvtZWarye+c68ZhQfMB1UsoRIYQBeFEI8RvgjcCzUsoHhRCfBj4N/HUa26rIMW7bVMGXnz6RvvD/HMBpMXA+hcdrGxibSBBV7jCzptzOqtIC9p7vp2vIx846F+sq7FRH0w3bTHp8wTDFBSa2VBdSV2wjFJa0DYzy8pleRpIonBwaF+lpOXhWlxVMSVB1LMURnYrUM6+Ay0iJkPHs9IboSwJ3ANdEl/8AeB4l4EuK2iIrH7p2Jf/x7KlsNyVtJFNZfj56RnzcuL6Mf717Cya9NmFz1mkRN1CvP8RoIMSX7t7CMreVTdWFMcP6R/0hHmls4UtPn2B4bOHtPDmtCEVLnxcppSqTlsPENYkphNABe4GVwNellLuFEGVSyg4AKWWHEKI0je1U5CjLS2zZbkJaSbWdv8pp4S9euxq31UiBSY9OEwRDYSyTAoYcFg2HxTBvrm2LUcd9l9Vxxapi/uKnBzjUOtPUs7zExtt21jIWCPHV351OqOJ8r8dPa//opKpEilwjrqtTShmSUm4FqoGdQoiN8Z5ACHG/EKJRCNHY3d2dZDMVucrqssTspKV2E3Zzfjg/aWJhNSpjodcJRsaCvPeHjTx15AJAUmaQyawoKeDh9+ziru1TBf+GdaW854rlvOfK5Xz4ulX87pNXs74isajKP53tXVDbFOkloV+SlHJACPE8cDPQKYSoiI6+K4CuWfZ5CHgIoKGhYfEaS5co6yoc3Li+jKePds65ncWg46PXr+KaNSWUOcy09nvp8/jp9/p56tVOnoyKWbawm/W8dWctD71wdmLZsiIbK0sLuKehBrtZjxCCp4908ttjF/tqN+n5+p9t52jHEL892knjPEmszvV6+frzp7lrRzXbap0AU6Jmk2+/gbsbqjnZOcyWmkJ21hXxui0VU8wf1S4r/3DnRu79zp64bxr7zverLIQ5jJhvhCGEKAECUfG2AE8DXwSuBnonTWK6pZR/NdexGhoaZGNjY4qarsgVzvV6eN+P9s6ZBe/+q5bzmVvXxVzn9Qd50zde5mgWJ80eed+l9Iz4+a8XzlLlNHP/VSvYOosb3fELQ/x8XxvHOoZwWY1ct7aU/ef7+bNLl/Hn33uFtnnKmn38hlW898rlEwW0U2VnllLSPezDpNdhN+vRZnE/fOiFM/zTr+PzHnLbjOz+zPUpyZSoSB4hxF4p5Yzsc/EI+GYik5Q6IiaXR6SUfyeEKAIeAWqB88DdUso5Cz0qAV+8SCl5/3/v5akjsUfiv/rIFWysKpx1/+GxAJ969OCs+6eby1YU8bev34DDYqDMkXih6sHRAIUWA+Gw5G9+eYQf/encjG121rnZtszJL/a38+HrVvK6zZUUWjNflmwsEOKTjxzk/w53xLX9N/5se0JVpxSpZzYBn/e2KqU8JKXcJqXcLKXcKKX8u+jyXinl9VLKVdH/VZXeJYwQgv/8sx18752XsLPePWVdfbFtTvGGiAng62/bzpuz8Lhu0AnWlNvRNJGUeEPErxoikYwSOREwU+2ycE9DNStKbKyvdPCeK5bzyRtX8/0/NrPzn37L4/vbUtaPeDEbdHz+despj7Ov/7MnlY6UilQy7wg8lagR+NIgFJb84VQ3H//pAaSMjL7j9WQYGgvwhq+9xNmezOUgf9/Vy/n49auneIIslEAozFeeOcn160rZsczNU0cusK3WSak9Iprnej38xU8P0FDnntW0lG66h308c7STzzx2eN5t/++jV7Chcu6bsCJ9JG1CSSVKwJcWo/4QbQOjrCyNL3XtOH/7xBG+91Jzeho1jds3V/CVN2/NuI3X4wui1wmMOi2rftZjgRA7//G3DM3jR37V6hJ++K6dGWqVYjpJm1AUimSxGHUJizfAh69dmXD+j7kw6TVM0/y5a9wWfvCunXztbduzMkFnM+kx6XVZD5Ix6bW4an++cLKb359UbsC5Rn445CqWFEUFJu7aXs1PG1tScjy9Jnj3lct5zfIi/nS2l0qnmTu2Vi2oaPFiQQjB1hrnvG6gAJ97/DC/++Q1yiMlh1DfhCIn+fB1K1N2LIfFwKGWAVaXFfCBa1bw5ktqlXhPonskvgIQnYM++jzJVxxSpB41AlfkJDVu60TSp4UgBHz73ga8gSCFFgN6NXqcQvewj4MtA3FtW15oTtpLR5Ee1NWsyFkeekcDV64qTmpfTcAHrlnBF163ASEEl9QVKfGOwStNvXHV+wRoHxjFk4bkXorkUVe0ImcptBr45zduwpqEe19YggxLrlhVzPpKVVV9Ns72enj/1Ssoshnn3TYYljxxsD0DrVLEixJwRU5T7bLy1p21Ce/3649eyV07qllRkrgXzFKiwmGha2iMN2yrimv7b7/YlPIEX4rkUQKuyHk+e+s6HrhlLcYETCCPNJ6fCJpRzM5tmyt4U0M133upKa7tT3eNcKJz9pw3isyiBFyR82ia4H1Xr2BdAqaQXfVFWckzkm8YdRr//ttTcdvBAZ7OUr4axUyUgCvyhgLT/LZwp9XAh69dyaoyZTqJB00TfPyGVQnt86tD7cqMkiMoAVfkDdesnr/o04A3wPELkTSvivi4bEUxly53z79hlJOdIxxpV/UycwEl4Iq8YU25HV0cIfZ3bqtGU3UcE+LPL6/HoBOU2E1xbf/rOFPRKtKLEnBF3nDV6hLunqdOJESCdxZapmypceP6Mt58SQ3Xr42vtO3+8wPpbZAiLpSAK/KKeMp7/XxfK2OBUAZas3gQQvCBq1fyh1M9cW3vD8VfHFmRPpSAK/KKTVWFrJ5ngrK514stjglPxVQcFj13bquKy10znsAfRfpRAq7IK4x6jZ9/8HK++fYds1aUOd01QvvAGEE1SkwIu9nAp25aw3uvqp93W5UTJTdQAq7IOwpMem7eWM7v/+oafviundQVzaz2s7upD19QCXgybJqn/B1AbZwVlhTpRWUjVOQtJr2Oq1aX8OuPXUlr/yiBUJiXz/Tyz785ziONLbxxW9VE5XdF/DTUudEEcwb3FBUoE0ouoK5uRd5jNepZXWYHYENlIesrHawrd+BSdtqkKC4w8dgHL6d9YJSP/Hg/wRhKblH51HMCZUJRLDouW1GsxHuBbKlxcsumCm7aUD5j3eu3VLJreVEWWqWYzrwCLoSoEUI8J4Q4JoQ4IoT4WHT5F4QQbUKIA9HXrelvrkKhyCTvv3rFjGW3ba7AYVYP77lAPN9CEPiklHKfEMIO7BVCPBNd9xUp5ZfS1zyFQpFNNlY5KC4w0jMSKaUmBEgpVXGMHGFeAZdSdgAd0ffDQohjQHzJgxUKRV4TlhGb+OoyO/5gmDdur2JrjSvbzVJESeg5SAhRB2wDdgOXAx8WQtwLNBIZpfenvIUKhSJr6DTBkx+/CoBQWNLn8cedL0WRfuJ+DhJCFAD/C3xcSjkEfANYAWwlMkL/8iz73S+EaBRCNHZ3dy+8xQqFIuOM+YP4gyEl3jlGXAIuhDAQEe+HpZQ/B5BSdkopQ1LKMPAtYGesfaWUD0kpG6SUDSUlJalqt0KhSCNdQ2P8v2dPcapzmN4RH5omsBjVxGWuMe83IoQQwHeAY1LKf5u0vCJqHwe4E3g1PU1UKBSZwB8M4wuGCIUlHYNjfOjalWhxpO9VZI94bqmXA+8ADgshDkSXfQZ4qxBiKyCBZuB9aWifQqHIEEa9hlEfeSh3qoIYeUE8XigvArFuw79OfXMUCoVCES/KmVOhUCjyFCXgCoVCkacoAVcoFIo8RQm4QqFQ5ClKwBUKhSJPUQKuUCgUeYoScIVCochTlIArFApFnqIEXKFQKPIUJeAKhUKRpygBVygUijxFCbhCoVDkKUrAFQqFIk9RAq5QKBR5ihJwhUKhyFOUgCsUCkWeogRcoVAo8hQl4AqFQpGnKAFXKBSKPEUJuEKhUOQpSsAVCoUiT5lXwIUQNUKI54QQx4QQR4QQH4sudwshnhFCnIr+70p/cxUKhUIxTjwj8CDwSSnlOuBS4ENCiPXAp4FnpZSrgGejfysUCoUiQ8wr4FLKDinlvuj7YeAYUAXcAfwgutkPgDekqY0KhUKhiEFCNnAhRB2wDdgNlEkpOyAi8kBpylunUCgUilmJW8CFEAXA/wIfl1IOJbDf/UKIRiFEY3d3dzJtVCgUCkUM4hJwIYSBiHg/LKX8eXRxpxCiIrq+AuiKta+U8iEpZYOUsqGkpCQVbVYoFAoF8XmhCOA7wDEp5b9NWvVL4L7o+/uAX6S+eQqFQqGYDX0c21wOvAM4LIQ4EF32GeBB4BEhxLuB88DdaWmhQqFQKGIyr4BLKV8ExCyrr09tcxQKhUIRLyoSU6FQKPIUJeAKhUKRpygBVygUijxFCbhCoVDkKfF4oSgUCoUiBsc6hjjcOkix3Ug4DEa9RpnDjMtqoKjAhE6bzf8jNSgBVygUiiT5yjMnefpoZ8x1y4qs/NOdm9hV76a1f5S6YlvKz69MKAqFQhEnobBESjnx9yV17lm3HfWHMOgEB1sHqHRa0tIeNQJXKBSKOOkcGqOowIhJrwOgY3AMp9VAcYGJcFgSDEvaBkYpd5j561vWsqGykEAojFGfnrGymHw3STcNDQ2ysbExY+dTKBSKdBIKS0JhOSHQUko8/hAFptSOjYUQe6WUDdOXqxG4QqFQJIlOE1MmKoUQKRfvuVA2cIVCochTlIArFApFnqIEXKFQKPIUJeAKhUKRpygBVygUijxFCbhCoVDkKUrAFQqFIk9RAq5QKBR5SkYjMYUQ3cC5jJ0wMYqBnmw3IoUspv6ovuQmqi+ZY5mUsmT6wowKeC4jhGiMFaqaryym/qi+5CaqL9lHmVAUCoUiT1ECrlAoFHmKEvCLPJTtBqSYxdQf1ZfcRPUlyygbuEKhUOQpagSuUCgUecqSFHAhxN1CiCNCiLAQomHS8tcKIfYKIQ5H/79u0rod0eWnhRBfFUKkt1ppnMzRlyIhxHNCiBEhxNem7ZNXfYmueyDa3hNCiJsmLc/JvkxGCLFFCPFytJ1PCCEck9bF7FeuIoTYKoT4kxDigBCiUQixc9K6vOoLgBDip9G+HBBCNAshDkxal/v9kVIuuRewDlgDPA80TFq+DaiMvt8ItE1atwd4DSCA3wC3ZLsf8/TFBlwBvB/42rR98q0v64GDgAmoB84Aulzuy7R+vQJcHX3/LuDv5+tXrr6Ap8c/Y+BW4Pl87UuMvn0Z+Hw+9WdJjsCllMeklCdiLN8vpWyP/nkEMAshTEKICsAhpXxZRr7dHwJvyFyLZ2eOvniklC8CY5OX52NfgDuAn0gpfVLKJuA0sDOX+zKNNcAL0ffPAHdF38fsVxbalwgSGH+CKATGfy/52JcJok9u9wA/ji7Ki/4sSQGPk7uA/VJKH1AFtE5a1xpdlo/kY1+qgJZJf4+3OV/68irw+uj7u4Ga6PvZ+pXLfBz4VyFEC/Al4IHo8nzsy2SuBDqllKeif+dFfxZtTUwhxG+B8hirPiul/MU8+24AvgjcOL4oxmYZc99ZSF9iHS7Gslzvy2xtzmpfJjNXv4iYTb4qhPg88EvAP75bjO2z7hY2T1+uB/5CSvm/Qoh7gO8AN5CjfYG4r7m3cnH0DTncn8ksWgGXUt6QzH5CiGrgMeBeKeWZ6OJWoHrSZtVcfHRMO8n2ZRbysS+tXBy1wsU2Z7Uvk4mjXzcCCCFWA7dFl83Wr6wyV1+EED8EPhb981Hg29H3OdkXmP+7EULogTcCOyYtztn+TEaZUCYhhHAC/wc8IKV8aXy5lLIDGBZCXBq1ld0LJDryzQnytC+/BN4SnY+oB1YBe/KlL0KI0uj/GvA54JvRVTH7lZ1Wxk07cHX0/XXAuMkhH/syzg3AcSnlZHNcfvQn27Oo2XgBdxK5w/qATuCp6PLPAR7gwKRXaXRdAxFb5hnga0SDoLL9mq0v0XXNQB8wEt1mfR735bPR9p5gkqdJrvZlWr8+BpyMvh6c3MbZ+pWrLyKeTXuJeGjsBnbka18mtfv7wPtjLM/5/qhITIVCochTlAlFoVAo8hQl4AqFQpGnKAFXKBSKPEUJuEKhUOQpSsAVCoUiT1ECrlAoFHmKEnCFQqHIU5SAKxQKRZ7y/wPj96xA5PIWogAAAABJRU5ErkJggg=="},"metadata":{"needs_background":"light"}}],"metadata":{}},{"cell_type":"markdown","source":["Now let's load in the data of hurricane Florence. This is stored in a plain-old CSV so we read it in with `pandas`."],"metadata":{},"attachments":{}},{"cell_type":"code","execution_count":5,"source":["florence = pd.read_csv('stormhistory.csv')\n","florence.head()"],"outputs":[{"output_type":"execute_result","data":{"text/plain":[" AdvisoryNumber Date Lat Long Wind Pres \\\n","0 1 08/30/2018 11:00 12.9 18.4 30 1007 \n","1 1A 08/30/2018 14:00 12.9 19.0 30 1007 \n","2 2 08/30/2018 17:00 12.9 19.4 30 1007 \n","3 2A 08/30/2018 20:00 13.1 20.4 30 1007 \n","4 3 08/30/2018 23:00 13.2 20.9 35 1007 \n","\n"," Movement Type Name Received \\\n","0 W at 12 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 10:45 \n","1 W at 12 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 13:36 \n","2 W at 9 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 16:36 \n","3 W at 11 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 19:44 \n","4 W at 13 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 22:42 \n","\n"," Forecaster \n","0 Avila \n","1 Avila \n","2 Avila \n","3 Beven \n","4 Beven "],"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
AdvisoryNumberDateLatLongWindPresMovementTypeNameReceivedForecaster
0108/30/2018 11:0012.918.4301007W at 12 MPH (280 deg)Potential Tropical CycloneSix08/30/2018 10:45Avila
11A08/30/2018 14:0012.919.0301007W at 12 MPH (280 deg)Potential Tropical CycloneSix08/30/2018 13:36Avila
2208/30/2018 17:0012.919.4301007W at 9 MPH (280 deg)Potential Tropical CycloneSix08/30/2018 16:36Avila
32A08/30/2018 20:0013.120.4301007W at 11 MPH (280 deg)Potential Tropical CycloneSix08/30/2018 19:44Beven
4308/30/2018 23:0013.220.9351007W at 13 MPH (280 deg)Potential Tropical CycloneSix08/30/2018 22:42Beven
\n","
"]},"metadata":{},"execution_count":5}],"metadata":{}},{"cell_type":"code","execution_count":6,"source":["florence.plot()"],"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":6},{"output_type":"display_data","data":{"text/plain":["
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this doesn't look like the path of hurricane Florence at all! In fact, this data viz doesn't really tell us much at all! The reason is this `pandas.DataFrame` doesn't have any notion of geospatial data. Even though there are columns for the longitude (`Long`) and the latitude (`Lat`), it is just plotting them as if they were any other numeric column!\n","\n","To fix this, we will need to turn it to `geopandas.GeoDataFrame`. In order to do this, we will need to construct a `Point` geometry since each row corresponds to a single point where the hurricane was. We will create a new column called `coordinates` that stores these `Point` objects from the `shapely` library of geometric shapes. The code to do this is relatively complex so do not worry too much about understanding it. If you're curious how we turn the `Lat` and `Long` columns to these points, we explain the following code:\n","* `zip` the `Long` and `Lat` column to get a series of `(long, lat)` pairs.\n","* Loop over this generator and create a `Point` for each one. As a technical note, we need to negate the longitude because our US dataset stores longitudes for the US as negative values.\n","* Store the resulting `Point`s in a new column of `florence.`"],"metadata":{},"attachments":{}},{"cell_type":"code","execution_count":7,"source":["from shapely.geometry import Point\n","\n","# Add new-lines for clarity\n","florence['coordinates'] = [\n"," Point(-long, lat) \n"," for long, lat \n"," in zip(florence['Long'], florence['Lat'])\n","]"],"outputs":[],"metadata":{}},{"cell_type":"markdown","source":["We can then convert this to a `geopandas.GeoDataFrame` with the following cell. Notice the new column `coordinates` stores the geometry for each row!"],"metadata":{},"attachments":{}},{"cell_type":"code","execution_count":8,"source":["florence = gpd.GeoDataFrame(florence, geometry='coordinates')\n","florence.head()"],"outputs":[{"output_type":"execute_result","data":{"text/plain":[" AdvisoryNumber Date Lat Long Wind Pres \\\n","0 1 08/30/2018 11:00 12.9 18.4 30 1007 \n","1 1A 08/30/2018 14:00 12.9 19.0 30 1007 \n","2 2 08/30/2018 17:00 12.9 19.4 30 1007 \n","3 2A 08/30/2018 20:00 13.1 20.4 30 1007 \n","4 3 08/30/2018 23:00 13.2 20.9 35 1007 \n","\n"," Movement Type Name Received \\\n","0 W at 12 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 10:45 \n","1 W at 12 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 13:36 \n","2 W at 9 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 16:36 \n","3 W at 11 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 19:44 \n","4 W at 13 MPH (280 deg) Potential Tropical Cyclone Six 08/30/2018 22:42 \n","\n"," Forecaster coordinates \n","0 Avila POINT (-18.40000 12.90000) \n","1 Avila POINT (-19.00000 12.90000) \n","2 Avila POINT (-19.40000 12.90000) \n","3 Beven POINT (-20.40000 13.10000) \n","4 Beven POINT (-20.90000 13.20000) "],"text/html":["
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AdvisoryNumberDateLatLongWindPresMovementTypeNameReceivedForecastercoordinates
0108/30/2018 11:0012.918.4301007W at 12 MPH (280 deg)Potential Tropical CycloneSix08/30/2018 10:45AvilaPOINT (-18.40000 12.90000)
11A08/30/2018 14:0012.919.0301007W at 12 MPH (280 deg)Potential Tropical CycloneSix08/30/2018 13:36AvilaPOINT (-19.00000 12.90000)
2208/30/2018 17:0012.919.4301007W at 9 MPH (280 deg)Potential Tropical CycloneSix08/30/2018 16:36AvilaPOINT (-19.40000 12.90000)
32A08/30/2018 20:0013.120.4301007W at 11 MPH (280 deg)Potential Tropical CycloneSix08/30/2018 19:44BevenPOINT (-20.40000 13.10000)
4308/30/2018 23:0013.220.9351007W at 13 MPH (280 deg)Potential Tropical CycloneSix08/30/2018 22:42BevenPOINT (-20.90000 13.20000)
\n","
"]},"metadata":{},"execution_count":8}],"metadata":{}},{"cell_type":"code","execution_count":9,"source":["florence.plot()"],"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":9},{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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"},"metadata":{"needs_background":"light"}}],"metadata":{}},{"cell_type":"markdown","source":["And now it prints out the path of the hurricane! Remember, understanding the details of the transformation from lat/long columns to a single column is not important. **What is important to understand is you need to make a `GeoDataFrame` if you want to leverage geospatial displays/processing.**"],"metadata":{},"attachments":{}},{"cell_type":"markdown","source":["## Plot the Hurricane\n","So now that we have our `country` data and `florence` data, let's try plotting them together to see where the hurricane hit the U.S. We pass in an extra parameters to the `florence` plot to make the dots black and a bit smaller."],"metadata":{},"attachments":{}},{"cell_type":"code","execution_count":10,"source":["country.plot()\n","florence.plot(color='black', markersize=10)"],"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":10},{"output_type":"display_data","data":{"text/plain":["
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"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["
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"},"metadata":{"needs_background":"light"}}],"metadata":{}},{"cell_type":"markdown","source":["That dooesn't look right! Why didn't they plot on top of each other? \n","\n","Remember, by default plotting functions generally make a new **figure** so each plot will end up being completely separate. To fix this, we need to make a figure with a single axes, and have both plots draw on that axis. We pass in an additional parameter `figsize` to make the figure slightly larger."],"metadata":{},"attachments":{}},{"cell_type":"code","execution_count":11,"source":["fig, ax = plt.subplots(1, figsize=(15, 7))\n","\n","country.plot(ax=ax)\n","florence.plot(color='black', markersize=10, ax=ax)"],"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":11},{"output_type":"display_data","data":{"text/plain":["
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"},"metadata":{"needs_background":"light"}}],"metadata":{}},{"cell_type":"markdown","source":["That looks much better! This can show the power of geospatial data, since it very naturally lets you overlap data if they occur in the same location in the world. \n","\n","Next time, we will pick up with this example and do something more complex that involves highlighting which states intersect with the hurricane's path!\n","\n","## Recap\n","* `geopandas.GeoDataFrame` lets you make plots that capture geospatial information.\n","* If you want to plot multiple plots on the same axes, you need to explicitly make a `Figure` and `Axes` and pass the axes in.\n","* (Less important) Unless your data comes in a geospatial format, converting them to one can be pretty tedious."],"metadata":{},"attachments":{}}]}