plot_health_data.ipynb

{
 "metadata": {
  "name": "plot_health_data"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "How to plot counties and shade them according with a specified color?\n",
      "\n",
      "\n",
      "\n",
      "I forgot that this has been an [old question for me](http://blog.dataunbound.com/2009/09/03/plotting-data-for-counties-on-google-maps-part-i/).\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Where to get county shape files?\n",
      "\n",
      "\n",
      "\n",
      "* A [UScounty shapefile is included in basemap version 1.0.6](https://github.com/matplotlib/basemap/commits/v1.0.6rel/lib/mpl_toolkits/basemap/data/UScounties.shp). Unfortunately, the latest version of basemap provided by enpkg is 1.0.2 (and I've not successfully been able to compile from source on a Mac).\n",
      "\n",
      "\n",
      "\n",
      "* I think it's safe to copy the USCounties files over from version 1.0.6 to your basemap data directory, but I'd rather not add files to that directory.\n",
      "\n",
      "\n",
      "\n",
      "* Alternative:  download files from US Census\n",
      "\n",
      "\n",
      "\n",
      "http://www.census.gov/geo/www/tiger/tgrshp2012/tgrshp2012.html\n",
      "\n",
      "\n",
      "\n",
      "ftp interface: ftp://ftp2.census.gov/geo/tiger/TIGER2012/COUNTY/ -> ftp://ftp2.census.gov/geo/tiger/TIGER2012/COUNTY/tl_2012_us_county.zip\n",
      "\n",
      "\n",
      "\n",
      "I've not added these files to the working-open-data repo because I don't want large data files in the repo if possible."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "\n",
      "\n",
      "with warnings.catch_warnings():\n",
      "\n",
      "    warnings.simplefilter(\"ignore\")\n",
      "\n",
      "\n",
      "\n",
      "    from mpl_toolkits.basemap import Basemap"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# you can figure out where the data directory for basemap is\n",
      "\n",
      "\n",
      "\n",
      "import os\n",
      "\n",
      "import inspect\n",
      "\n",
      "basemap_data_dir = os.path.join(os.path.dirname(inspect.getfile(Basemap)), \"data\")\n",
      "\n",
      "print basemap_data_dir, os.path.exists(os.path.join(basemap_data_dir,\"UScounties.shp\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "C:\\Python27\\lib\\site-packages\\mpl_toolkits\\basemap\\data True\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls $basemap_data_dir"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "'ls' is not recognized as an internal or external command,\n",
        "operable program or batch file.\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# https://code.google.com/p/pyshp/\n",
      "\n",
      "import shapefile # install shpf module for shape files\n",
      "\n",
      " \n",
      "\n",
      "# this is my git clone of https://github.com/matplotlib/basemap --> these files will be in the PiCloud basemap_data_dir\n",
      "\n",
      "if os.path.exists(os.path.join(basemap_data_dir,\"UScounties.shp\")):\n",
      "\n",
      "    shpf = shapefile.Reader(os.path.join(basemap_data_dir,\"UScounties\"))\n",
      "\n",
      "else:\n",
      "\n",
      "    # put in your path\n",
      "\n",
      "    #shpf = shapefile.Reader(\"/Users/raymondyee/Dropbox/WwoD13/tl_2012_us_county\")\n",
      "\n",
      "    shpf = shapefile.Reader(\"/Users/raymondyee/C/src/basemap/lib/mpl_toolkits/basemap/data/UScounties\")\n",
      "\n",
      "\n",
      "\n",
      "shapes = shpf.shapes()\n",
      "\n",
      "records = shpf.records()\n",
      "\n",
      "for x in records:\n",
      "    print x    \n",
      "    break\n",
      "\n",
      "for y in shapes:\n",
      "    print y\n",
      "    break\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['01', '029', '01029', 'AL', 'Cleburne', 'County']\n",
        "<shapefile._Shape instance at 0x052AF468>\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "shpf.fields\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[('DeletionFlag', 'C', 1, 0),\n",
        " ['STATE_FIPS', 'C', 2, 0],\n",
        " ['COUNTY_FIP', 'C', 3, 0],\n",
        " ['FIPS', 'C', 5, 0],\n",
        " ['STATE', 'C', 2, 0],\n",
        " ['NAME', 'C', 25, 0],\n",
        " ['LSAD', 'C', 10, 0]]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "zip([f[0] for f in shpf.fields[1:]], records[0])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[('STATE_FIPS', '01'),\n",
        " ('COUNTY_FIP', '029'),\n",
        " ('FIPS', '01029'),\n",
        " ('STATE', 'AL'),\n",
        " ('NAME', 'Cleburne'),\n",
        " ('LSAD', 'County')]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# just CA\n",
      "\n",
      "\n",
      "\n",
      "from itertools import islice, izip\n",
      "\n",
      "len([r for r in islice(records,None) if r[0] == '06'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "58"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def zip_filter_by_state(records, shapes, included_states=None):\n",
      "\n",
      "    # by default, no filtering\n",
      "\n",
      "    # included_states is a list of states fips prefixes\n",
      "\n",
      "    for (record, state) in izip(records, shapes):\n",
      "\n",
      "        if record[0] in included_states:\n",
      "\n",
      "            yield (record, state) \n",
      "\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "list(zip_filter_by_state(records, shapes, ['06']))[:2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "[(['06', '035', '06035', 'CA', 'Lassen', 'County'],\n",
        "  <shapefile._Shape instance at 0x0581EC88>),\n",
        " (['06', '049', '06049', 'CA', 'Modoc', 'County'],\n",
        "  <shapefile._Shape instance at 0x0581ECD8>)]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(shapes)\n",
      "\n",
      "import pandas as pd\n",
      "df = pd.read_csv('Tracking_Hospital_2007_CountyMeasures.csv') # change file to plot map\n",
      "df = df.set_index('cnty_fips')\n",
      "df = df[df['Age Adj. Rate per 10000'] != 'null']\n",
      "df['Age Adj. Rate per 10000'] = df['Age Adj. Rate per 10000'].apply(lambda x:float(x)) # convert series to float\n",
      "df_series = df['Age Adj. Rate per 10000']\n",
      "maxval = df_series.max()\n",
      "minval = df_series.min()\n",
      "\n",
      "def normalize(x):\n",
      "\tglobal maxval\n",
      "\tglobal minval\n",
      "\tnormval = float((x-minval)/(maxval-minval))\n",
      "\treturn normval\n",
      "\n",
      "\n",
      "\n",
      "def lookupfips(x):\n",
      "    global df_series\n",
      "    if len(x) == 5:\n",
      "        x = x[1:]\n",
      "    x = float(x)\n",
      "    try:\n",
      "        val = df_series[x]\n",
      "        return val\n",
      "    except KeyError:\n",
      "        print \"lookupfailed\"\n",
      "        return 0\n",
      "        \n",
      "    \n",
      "\n",
      "df_series = df_series.apply(normalize)\n",
      "print lookupfips('06005')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.298059964727\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# http://www.geophysique.be/2013/02/12/matplotlib-basemap-tutorial-10-shapefiles-unleached-continued/\n",
      "#\n",
      "# BaseMap example by geophysique.be\n",
      "# tutorial 10\n",
      "import time\n",
      "\n",
      "\n",
      "import os\n",
      "\n",
      "import inspect\n",
      "\n",
      "import numpy as np\n",
      "\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "from itertools import islice, izip\n",
      "\n",
      "from mpl_toolkits.basemap import Basemap\n",
      "\n",
      "from matplotlib.collections import LineCollection\n",
      "\n",
      "from matplotlib import cm\n",
      "\n",
      "import shapefile \n",
      "\n",
      " \n",
      "\n",
      "### PARAMETERS FOR MATPLOTLIB :\n",
      "\n",
      "import matplotlib as mpl\n",
      "\n",
      "mpl.rcParams['font.size'] = 10.\n",
      "\n",
      "mpl.rcParams['font.family'] = 'Comic Sans MS'\n",
      "\n",
      "mpl.rcParams['axes.labelsize'] = 8.\n",
      "\n",
      "mpl.rcParams['xtick.labelsize'] = 6.\n",
      "\n",
      "mpl.rcParams['ytick.labelsize'] = 6.\n",
      "\n",
      " \n",
      "\n",
      "fig = plt.figure(figsize=(11.7,11)) #11.7,8.3\n",
      "\n",
      "#Custom adjust of the subplots\n",
      "\n",
      "plt.subplots_adjust(left=0.05,right=0.95,top=0.90,bottom=0.05,wspace=0.15,hspace=0.05)\n",
      "\n",
      "ax = plt.subplot(111)\n",
      "ax.set_title('County-wise distribution for California State - Hospitalizations')\n",
      "\n",
      "#Let's create a basemap of USA\n",
      "\n",
      "#x1 = -180.\n",
      "#x2 = -62.\n",
      "#y1 = 18.\n",
      "#y2 = 68.\n",
      "\n",
      "# co-ordinates of california state\n",
      "x1 = -128.\n",
      "x2 = -114.\n",
      "y1 = 32.\n",
      "y2 = 43.\n",
      " \n",
      "#m = Basemap(llcrnrlon=-128,llcrnrlat=25,urcrnrlon=-111.0,urcrnrlat=44.5,\n",
      "#            resolution='i',projection='cass',lon_0=-120.36,lat_0=37.7)\n",
      "m = Basemap(resolution='i',projection='merc', llcrnrlat=y1,urcrnrlat=y2,llcrnrlon=x1,urcrnrlon=x2,lat_ts=(y1+y2)/2)\n",
      "\n",
      "#m.drawcountries(linewidth=0.5)\n",
      "\n",
      "#m.drawcoastlines(linewidth=0.5)\n",
      "\n",
      "m.drawparallels(np.arange(y1,y2,2.),labels=[1,0,0,0],color='black',dashes=[1,0],labelstyle='+/-',linewidth=0.2) # draw parallels\n",
      "\n",
      "m.drawmeridians(np.arange(x1,x2,2.),labels=[0,0,0,1],color='black',dashes=[1,0],labelstyle='+/-',linewidth=0.2) # draw meridians\n",
      "    \n",
      "\n",
      "def zip_filter_by_state(records, shapes, included_states=None):\n",
      "\n",
      "    # by default, no filtering\n",
      "\n",
      "    # included_states is a list of states fips prefixes\n",
      "\n",
      "    for (record, state) in izip(records, shapes):\n",
      "\n",
      "        if record[0] in included_states:\n",
      "\n",
      "            yield (record, state) \n",
      "\n",
      "    \n",
      "\n",
      "    \n",
      "\n",
      "\n",
      "\n",
      "basemap_data_dir = os.path.join(os.path.dirname(inspect.getfile(Basemap)), \"data\")\n",
      "\n",
      "\n",
      "\n",
      "# this is my git clone of https://github.com/matplotlib/basemap --> these files will be in the PiCloud basemap_data_dir\n",
      "\n",
      "if os.path.exists(os.path.join(basemap_data_dir,\"UScounties.shp\")):\n",
      "\n",
      "    shpf = shapefile.Reader(os.path.join(basemap_data_dir,\"UScounties\"))\n",
      "\n",
      "else:\n",
      "\n",
      "    # put in your path\n",
      "\n",
      "    #shpf = shapefile.Reader(\"/Users/raymondyee/Dropbox/WwoD13/tl_2012_us_county\")\n",
      "\n",
      "    shpf = shapefile.Reader(\"/Users/raymondyee/C/src/basemap/lib/mpl_toolkits/basemap/data/UScounties\")\n",
      "\n",
      "\n",
      "\n",
      "shapes = shpf.shapes()\n",
      "\n",
      "records = shpf.records()\n",
      "\n",
      " \n",
      "\n",
      "# show only CA and AK (for example)\n",
      "\n",
      "for record, shape in zip_filter_by_state(records, shapes, ['06']):# removed 02 state , we just need california\n",
      "\n",
      "    lons,lats = zip(*shape.points) # lat long for each county\n",
      "\n",
      "    data = np.array(m(lons, lats)).T\n",
      "    \n",
      "    \n",
      "\n",
      " \n",
      "\n",
      "    if len(shape.parts) == 1:\n",
      "\n",
      "        segs = [data,]\n",
      "\n",
      "    else:\n",
      "\n",
      "        segs = []\n",
      "\n",
      "        for i in range(1,len(shape.parts)):\n",
      "\n",
      "            index = shape.parts[i-1]\n",
      "\n",
      "            index2 = shape.parts[i]\n",
      "\n",
      "            segs.append(data[index:index2])\n",
      "            \n",
      "            #time.sleep(1) # introduce a delay of 1 second\n",
      "\n",
      "        segs.append(data[index2:])\n",
      "\n",
      " \n",
      "\n",
      "    lines = LineCollection(segs,antialiaseds=(1,))\n",
      "    \n",
      "    color = lookupfips(record[2])\n",
      "    \n",
      "    \n",
      "    lines.set_label('C') \n",
      "    lines.set_facecolors(cm.gist_yarg(color)) #cm.jet(np.random.rand(1))\n",
      "    #print cm.Blues(0), binary \n",
      "    lines.set_edgecolors('k')\n",
      "    lines.set_linewidth(0.1)       \n",
      "    ax.add_collection(lines) # draw step\n",
      "    \n",
      "    \n",
      "\n",
      " \n",
      "\n",
      "plt.savefig('tutorial10.png',dpi=300)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "lookupfailed\n",
        "lookupfailed\n",
        "lookupfailed\n",
        "lookupfailed"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "lookupfailed\n",
        "lookupfailed"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "lookupfailed\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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6dOgQduzYEZG+iIiI4hWTWYorGzduhE4Xmae90+nEyJEjI9IXDZ2iKBGbGBhr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\n"
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "cnty_fips\n",
        "6000         0.400544\n",
        "6001         0.699388\n",
        "6005         0.479674\n",
        "6007         0.371176\n",
        "6009         0.478314\n",
        "6011         0.189123\n",
        "6013         0.631951\n",
        "6015         0.638477\n",
        "6017         0.254657\n",
        "6019         0.643100\n",
        "6021         0.157444\n",
        "6023         0.635758\n",
        "6025         1.000000\n",
        "6027         0.326581\n",
        "6029         0.392522\n",
        "6031         0.610741\n",
        "6033         0.751734\n",
        "6035         0.287831\n",
        "6037         0.405167\n",
        "6039         0.666349\n",
        "6041         0.188987\n",
        "6043         0.433447\n",
        "6045         0.641332\n",
        "6047         0.657512\n",
        "6049         0.282393\n",
        "6051         0.000000\n",
        "6053         0.397689\n",
        "6055         0.332019\n",
        "6057         0.581509\n",
        "6059         0.211013\n",
        "6061         0.140857\n",
        "6063         0.840245\n",
        "6065         0.344256\n",
        "6067         0.505506\n",
        "6069         0.441740\n",
        "6071         0.529164\n",
        "6073         0.328348\n",
        "6075         0.281441\n",
        "6077         0.541808\n",
        "6079         0.293134\n",
        "6081         0.353229\n",
        "6083         0.151462\n",
        "6085         0.208702\n",
        "6087         0.299116\n",
        "6089         0.469477\n",
        "6091         0.642556\n",
        "6093         0.316383\n",
        "6095         0.622706\n",
        "6097         0.396601\n",
        "6099         0.395785\n",
        "6101         0.255608\n",
        "6103         0.776886\n",
        "6105         0.457920\n",
        "6107         0.463222\n",
        "6109         0.671924\n",
        "6111         0.285248\n",
        "6113         0.272740\n",
        "6115         0.482529\n",
        "Name: Age Adj. Rate per 10000, Length: 58\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from mpl_toolkits.basemap import Basemap\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "# llcrnrlat,llcrnrlon,urcrnrlat,urcrnrlon\n",
      "# are the lat/lon values of the lower left and upper right corners\n",
      "# of the map.\n",
      "# resolution = 'i' means use intermediate resolution coastlines.\n",
      "# lon_0, lat_0 are the central longitude and latitude of the projection.\n",
      "#m = Basemap(llcrnrlon=-10.5,llcrnrlat=49.5,urcrnrlon=3.5,urcrnrlat=59.5,\n",
      "#            resolution='i',projection='cass',lon_0=-4.36,lat_0=54.7)\n",
      "m = Basemap(llcrnrlon=-128,llcrnrlat=25,urcrnrlon=-111.0,urcrnrlat=44.5,\n",
      "            resolution='i',projection='cass',lon_0=-120.36,lat_0=37.7)\n",
      "# can get the identical map this way (by specifying width and\n",
      "# height instead of lat/lon corners)\n",
      "#m = Basemap(width=891185,height=1115557,\\\n",
      "#            resolution='i',projection='cass',lon_0=-4.36,lat_0=54.7)\n",
      "m.drawcoastlines()\n",
      "m.fillcontinents(color='coral',lake_color='aqua')\n",
      "# draw parallels and meridians.\n",
      "#m.drawparallels(np.arange(-40,61.,2.))\n",
      "#m.drawmeridians(np.arange(-20.,21.,2.))\n",
      "m.drawmapboundary(fill_color='aqua')\n",
      "plt.title(\"Cassini Projection\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 16
    }
   ],
   "metadata": {}
  }
 ]
}