USGS_WaterUseStatistics_Summary_CONUS_humanUses.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summarize USGS Water Use Statistics by County"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shan Zuidema \n",
    "#### Water Systems Analysis Group, part of the Earth Systems Research Center\n",
    "#### in the Institute for the study of Earth, Oceans, and Space  at the University of New Hampshire"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Copyright 2021, University of New Hampshire\n",
    "    \n",
    "    This program is free software: you can redistribute it and/or modify\n",
    "    it under the terms of the GNU General Public License as published by\n",
    "    the Free Software Foundation, either version 3 of the License, or\n",
    "    (at your option) any later version.\n",
    "\n",
    "    This program is distributed in the hope that it will be useful,\n",
    "    but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
    "    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
    "    GNU General Public License for more details.\n",
    "\n",
    "    You should have received a copy of the GNU General Public License\n",
    "    along with this program.  If not, see <https://www.gnu.org/licenses/>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The United States Geological Survey (USGS) publishes water use statistics every five years for each county in the United States and territories.  The data are provided in a variety of formats (tab delimited text, excel spreadsheets) with corresponding metadata provided in accompanying tables or hosted as HTML on their websites.  Making estimates of how water-use is changing within the United States is complicated for a number of reasons.\n",
    "\n",
    "1. The definitions of water-use categories and record-keeping changed over-time.\n",
    "2. The water use statistics themselves are accumulated through agencies and institutions and may exhibit spatial variability resulting purely from differences in reporting practices and procedures.\n",
    "3. The variety of formats, and field names make introspection challenging.\n",
    "\n",
    "This notebook attempts to reduce the burden from the third challenge, with a specific set of documented assumptions to address the first challenge in order to create a continuous record of water use records.  It collates county level data from 1985 through 2015 (and plans to be updated if necessary with future data releases).  The final summary of data is saved to disk as a comma-seperated-values (csv) file easily readable via a number of utilities.  An example is provided to read the resulting table with Pandas (Python).  Furthermore, derived fields are calculated.  Optional cells near the end of the notebook compile statistics into a GeoJSON file using the fiona library (if available),and display an example field using Shapely and Cartopy libraries.\n",
    "\n",
    "Notes: \n",
    "\n",
    "> Does not include statewide data available prior to 1985. \n",
    "\n",
    "> The utility does not supply the underlying data, nor does it provide (at this time) automatic fetching of the data.  It is considered the user's responsibility to retrieve the datasets (whole datasets, not individual states) and place them in a single folder. \n",
    "\n",
    "The variable `data_source_folder` (aka `fldr`) must be a string to be entered by the user that tells the utility where to find these data.\n",
    "\n",
    "Sources:\n",
    "\n",
    "https://water.usgs.gov/watuse/data/\n",
    "USGS data is in the Public Domain.\n",
    "\n",
    "These data are described by the following publications:\n",
    ">Dieter, C.A., Maupin, M.A., Caldwell, R.R., Harris, M.A., Ivahnenko, T.I., Lovelace, J.K., Barber, N.L., Linsey, K.S., 2018. Estimated use of water in the United States in 2015 (USGS Numbered Series No. 1441), Circular. U.S. Geological Survey, Reston, VA.\n",
    "\n",
    ">Maupin, M.A., Kenny, J.F., Hutson, S.S., Lovelace, J.K., Barber, N.L., Linsey, K.S., 2014. Estimated use of water in the United States in 2010, Circular. U.S. Geological Survey, Reston, Virginia."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The author provides no warranty regarding the veracity of the underlying data provided to or summarized by the USGS, and is in no way affiliated with the USGS. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import defaultdict,OrderedDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# our data source folder is used globally.  \n",
    "fldr=data_source_folder=\"/mnt/c/Data/data/USGSUSCO/\"\n",
    "output_file_name=fldr+\"USGS_waterUseStatistics_HumanUse_1985_2015.v2.csv\"\n",
    "county_folder=\"/mnt/c/Data/data/cb_2015_us_county_500k/\" # needed if GeoJSON or mapping is implemented\n",
    "county_fname=county_folder+\"cb_2015_us_county_500k.shp\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mgd2kmy = 3.78541178e-6 * 365.256"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Water-Use categories of interest\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Units**\n",
    "\n",
    "*Population*\n",
    "\n",
    "original units = population in thousands persons\n",
    "\n",
    "output units   = population in persons\n",
    "\n",
    "*Supply, Delivery, or Use fluxes*\n",
    "\n",
    "original units = Million gallons per day ($M gal\\ d^{-1}$)\n",
    "\n",
    "output units   = cubic kilometers per year ($km^3 y^{-1}$)\n",
    "\n",
    "**Comments**\n",
    "\n",
    "*DomesticTotalUse*\n",
    "\n",
    "Data for year 2000 is not provided.  We calculate this value by adding the DomesticSelfSupplied usage (provided) plus the product of the PublicSuppliedPopulation (provided) and a per capita PublicSupply rate (estimated).  The per capita PublicSupply rate for 2000 is calculated as the average per capita PublicSupply rate from 1995 and 2005 for each county.\n",
    "\n",
    "*IndustrialTotalUse*\n",
    "\n",
    "Commercial use (provided for 1985 though 1995) is included as IndustrialUse to maintain a consistent record, and follows from the high level disaggregation presented by the USGS [in this table](https://www.usgs.gov/mission-areas/water-resources/science/changes-water-use-categories?qt-science_center_objects=0#qt-science_center_objectsI).  However, Commercial use is most likely a combination of domestic and industrial uses, which is evident by discontinuities in Domestic and Industrial uses for individual counties before and after 1995.  An appropriate disaggregation of Commercial use to both Domestic and Industrial uses on a county by county basis is beyond the scope of this work.\n",
    "\n",
    "Data for years 2000 through 2015 provides only IndustrialSelfSuppliedUse.  To calculate IndustrialTotalUse we add IndustrialSelfSuppliedUse with the difference between PublicSupplyUse and DomesticPublicDeliveries. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build field names\n",
    "Some fields are not provided by the USGS for given years, but can be back-calculated with other data and the assumptions described above.  \n",
    "\n",
    "Entries of `ifunc` and `ofunc` tell the program how to calculate these missing fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Uses=OrderedDict(Population={2015:'TP-TotPop',2010:'TP-TotPop',2005:'TP-TotPop',2000:'TP-TotPop',1995:'TotalPop',1990:'po-total',1985:'po-total' },\n",
    "          DomesticSelfSupp={2015: 'DO-WFrTo', 2010: 'DO-WFrTo', 2005: 'DO-WFrTo', 2000:\"DO-WFrTo\",1995:'DO-WFrTo',1990:'do-sstot',1985:'do-sstot'},\n",
    "          DomesticSelfPop={2015: 'DO-SSPop', 2010: 'DO-SSPop', 2005: 'DO-SSPop', 2000:\"DO-SSPop\",1995:'DO-SSPop',1990:'do-sspop',1985:'do-sspop'},\n",
    "          DomesticTotalUse={2015:'DO-WDelv',2010:'DO-TOTAL',2005:'DO-TOTAL',\n",
    "                            2000:\"ofunc(DomesticTotal_2000)\",\n",
    "                            1995:'DO-WDelv',1990:'do-total',1985:'do-total'},\n",
    "          DomesticPubDeliv={2015:'DO-PSDel',2010:'DO-PSDel',2005:'DO-PSDel',\n",
    "                            2000:\"ofunc(DomesticPubDeliv_2000)\",\n",
    "                            1995:'DO-PSDel',1990:'do-psdel',1985:'do-psdel'},\n",
    "          PublicSupplyPop={2015: 'PS-TOPop', 2010: 'PS-TOPop', 2005: 'PS-TOPop', 2000:'PS-TOPop',\n",
    "                           1995:'PS-TOPop',1990:'ps-popto',1985:'ps-popto'},\n",
    "          PublicSupplyUse={2015:'PS-Wtotl',2010:'PS-Wtotl',2005:'PS-Wtotl',2000:'PS-WFrTo',\n",
    "                           1995:'PS-WFrTo',1990:'ps-wtofr',1985:'ps-wtofr'},\n",
    "          IndustrialSelfSupp={2015: 'IN-WFrTo',2010:'IN-WFrTo',2005:'IN-WFrTo',2000:'IN-WFrTo',\n",
    "                              1995:'IN-WFrTo',1990:'in-wtofr',1985:'in-wtofr'},\n",
    "          IndustrialTotalUse={2015:\"ofunc(IndustrialTotal)\",\n",
    "                              2010: \"ofunc(IndustrialTotal)\",\n",
    "                              2005: \"ofunc(IndustrialTotal)\",\n",
    "                              2000: \"ofunc(IndustrialTotal_2000)\",\n",
    "                              1995:'ifunc(IN-WDelv,add,CO-WDelv)',1990:'ifunc(in-total,add,co-total)',1985:'ifunc(in-total,add,co-total)'},\n",
    "          IrrigationTotalUse={2015: 'IR-WFrTo',2010: 'IR-WFrTo', 2005: 'IR-WFrTo',2000:'IT-WFrTo',\n",
    "                              1995:'IR-WFrTo',1990:'ir-frtot',1985:'ir-frtot'},\n",
    "          IrrigationSurfaceUse={2015: 'IR-WSWFr', 2010: \"IR-WSWFr\", 2005:\"IR-WSWFr\",2000:\"IT-WSWFr\",1995:\"IR-WSWFr\",1990:\"IR-WSWFr\".lower(),1985:\"IR-WSWFr\".lower()},\n",
    "          IrrigationGroundWUse={2015: 'IR-WGWFr', 2010: \"IR-WGWFr\", 2005: \"IR-WGWFr\",2000:\"IT-WGWFr\",1995:\"IR-WGWFr\",1990:\"IR-WGWFr\".lower(),1985:\"IR-WGWFr\".lower()},\n",
    "          LivestockTotalUse={2015: 'LI-WFrTo', 2010: 'LI-WFrTo', 2005: 'LS-WFrTo',2000:'LS-WFrTo',\n",
    "                             1995:'LS-WFrTo',1990:'ls-total',1985:'ls-total'} )\n",
    "# Lists of fluxes to convert and output\n",
    "volUses=\"DomesticSelfSupp DomesticTotalUse DomesticPubDeliv PublicSupplyUse IndustrialSelfSupp IndustrialTotalUse IrrigationTotalUse LivestockTotalUse IrrigationSurfaceUse IrrigationGroundWUse\".split()\n",
    "popUses=\"Population DomesticSelfPop PublicSupplyPop\".split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Selection of functions to parse individual year files in common formats.  Each returns a raw pandas dataframe.\n",
    "\n",
    "def read_1985_text(year,uses):\n",
    "    #1985 1990\n",
    "    cols=\"scode area\".split()+[use[year] for use in uses.values() if \"func\" not in use[year]]\n",
    "    \"\"\"for use in uses.values():\n",
    "        if 'ifunc' in use[year]:\n",
    "            for y in use[year].split(','):\n",
    "                if y is not 'add':\n",
    "                    cols.append(y)\"\"\"\n",
    "                    \n",
    "    cols+=[y for use in uses.values() if \"ifunc\" in use[year] for y in use[year].split('(')[1].split(')')[0].split(',') if y !='add']\n",
    "    df = pd.read_csv(fldr+\"us{}co.txt\".format(str(year)[-2:]),sep=\"\\t\",\\\n",
    "                          usecols=cols,\\\n",
    "                          dtype={\"area\":str,\"scode\":str})\n",
    "    df[\"FIPS\"]=df['scode']+df['area']\n",
    "    df[\"FIPS\"]=df[\"FIPS\"].astype(str)    \n",
    "    return df\n",
    "\n",
    "def read_1995_text(year,uses):\n",
    "    #1995\n",
    "    cols=\"StateCode CountyCode\".split()+[use[year] for use in uses.values() if \"func\" not in use[year]]\n",
    "    cols+=[y for use in uses.values() if \"ifunc\" in use[year] for y in use[year].split('(')[1].split(')')[0].split(',') if y !='add']\n",
    "    #print pd.read_csv(fldr+\"us{}co.txt\".format(state,str(year)[-2:]),sep=\"\\t\")\n",
    "    df = pd.read_csv(fldr+\"us{}co.txt\".format(str(year)[-2:]),sep=\"\\t\",\\\n",
    "                          usecols=cols,\\\n",
    "                          dtype={\"CountyCode\":str,\"StateCode\":str})\n",
    "    df[\"FIPS\"]=df['StateCode']+df['CountyCode']\n",
    "    df[\"FIPS\"]=df[\"FIPS\"].astype(str)\n",
    "    return df\n",
    "\n",
    "def read_2000_excel(year,uses):\n",
    "    # Parses water uses for the 200{0}'s\n",
    "    #2000 2005\n",
    "    cols=[\"FIPS\"]+[use[year] for use in uses.values() if \"func\" not in use[year]]\n",
    "    #print pd.read_csv(fldr+\"us{}co.txt\".format(state,str(year)[-2:]),sep=\"\\t\")\n",
    "    df = pd.read_csv(fldr+\"usco{}.txt\".format(year),sep=\"\\t\",\\\n",
    "                          usecols=cols,\\\n",
    "                          dtype={\"FIPS\":str})\n",
    "    fipser=lambda x: \"0\"+x if len(x)== 4 else x\n",
    "    df[\"FIPS\"]=df[\"FIPS\"].astype(str).apply(fipser)\n",
    "    return df\n",
    "    \n",
    "def read_2010(year,uses):\n",
    "    #2010\n",
    "    cols=[\"FIPS\"]+[use[year] for use in uses.values() if \"func\" not in use[year]]\n",
    "    #print pd.read_csv(fldr+\"us{}co.txt\".format(state,str(year)[-2:]),sep=\"\\t\")\n",
    "    df = pd.read_csv(fldr+\"usco{}.dat\".format(year),sep=\"\\t\",\\\n",
    "                          usecols=cols,\\\n",
    "                          dtype={\"FIPS\":str})\n",
    "    df[\"FIPS\"]=df[\"FIPS\"].astype(str)\n",
    "    return df\n",
    "def read_2015(year,uses):\n",
    "    #2015\n",
    "    cols=[\"FIPS\"]+[use[year] for use in uses.values() if \"func\" not in use[year]]\n",
    "    #print pd.read_csv(fldr+\"us{}co.txt\".format(state,str(year)[-2:]),sep=\"\\t\")\n",
    "    df = pd.read_csv(fldr+\"usco{}v2.csv\".format(year),usecols=cols,\\\n",
    "                          dtype={\"FIPS\":str},skiprows=1)\n",
    "    \n",
    "    fipser=lambda x: \"0\"+x if len(x)== 4 else x\n",
    "    df[\"FIPS\"]=df[\"FIPS\"].astype(str).apply(fipser)\n",
    "    return df\n",
    "\n",
    "def rdr(year,uses,vol_uses,pop_uses):\n",
    "    # Processes data for a given year and returns a clean dataframe \n",
    "    if (year < 1985) | (year % 5 != 0):\n",
    "        raise \"No water use data for year {}\".format(year)\n",
    "    if (year >= 1985) & (year < 1994):\n",
    "        df= read_1985_text(year,uses)\n",
    "    if (year == 1995):\n",
    "        df= read_1995_text(year,uses)\n",
    "    if (year in [2000,2005]):\n",
    "        df= read_2000_excel(year,uses)\n",
    "    if (year == 2010):\n",
    "        df=read_2010(year,uses)\n",
    "    if (year == 2015):\n",
    "        df= read_2015(year,uses)   \n",
    "        #1985 1990\n",
    "    # Conform each returned dataframe to a common format\n",
    "    renamer=dict([(use[year],key) for key,use in uses.items() if \"func\" not in use[year]])\n",
    "    df = df.rename(columns=renamer)\n",
    "    # Make necessary calculations from multiple data fields when missing\n",
    "    for name,use in uses.items():\n",
    "        if 'ifunc' in use[year]:\n",
    "            (first,func,second)=use[year].split('(')[1].split(')')[0].split(',')\n",
    "            assert func == 'add',\"Inner field calculation calls for unsupported operator\"\n",
    "            df[name]=df[first].add(df[second]) # fortunately all functions are additive\n",
    "    df[\"FIPS\"]=df['FIPS'].astype(str)\n",
    "    df.set_index(\"FIPS\",inplace=True)\n",
    "    # Unit conversions\n",
    "    df[list( set(df.columns) & set(vol_uses) )] *= mgd2kmy\n",
    "    df[list( set(df.columns) & set(pop_uses) )] *= 1000 # population\n",
    "    # Remove unused columns\n",
    "    addCols=list( set(uses.keys()) - set(df.columns))    \n",
    "    for col in addCols:\n",
    "        df[col]=pd.Series(dtype=float)\n",
    "    for col in df.columns:\n",
    "        if col.endswith('_A'):\n",
    "            df[col]=df[col[:-2]+'_B']\n",
    "    return df.loc[:,uses.keys()]\n",
    "\n",
    "# Define outer function calculations\n",
    "def DomPubDeliv2000(df,year=2000):\n",
    "    # In 2000 We nned to use data from 1995 and 2005 to estimate\n",
    "    x1=df.loc[(slice(None),year),'PublicSupplyPop']\n",
    "    x2=df.loc[(slice(None),[1995,2005]),'PublicSupplyUse'].sum(level=0)\n",
    "    x3=df.loc[(slice(None),[1995,2005]),'PublicSupplyPop'].sum(level=0)\n",
    "    x=x1*x2/x3\n",
    "    return x\n",
    "\n",
    "def apply_ofunc(df,fun_nm,year):\n",
    "    funcs={'ofunc(DomesticTotal_2000)':df.loc[(slice(None),year),'DomesticSelfSupp']+DomPubDeliv2000(df),\n",
    "           'ofunc(DomesticPubDeliv_2000)':DomPubDeliv2000(df),\n",
    "           'ofunc(IndustrialTotal)':(df.loc[(slice(None),year),'PublicSupplyUse']- df.loc[(slice(None),year),'DomesticPubDeliv']).clip(lower=0.0)\\\n",
    "           +df.loc[(slice(None),year),'IndustrialSelfSupp'],\n",
    "           'ofunc(IndustrialTotal_2000)':(df.loc[(slice(None),year),'PublicSupplyUse']- DomPubDeliv2000(df)).clip(lower=0)+df.loc[(slice(None),year),'IndustrialSelfSupp']}\n",
    "    return funcs[fun_nm]\n",
    "\n",
    "def calc_dependencies(df,uses,ys):\n",
    "    for nm,use in uses.items():\n",
    "        for y in ys:\n",
    "            if 'ofunc' in use[y]:\n",
    "                df.loc[(slice(None),y),nm]=apply_ofunc(df,use[y],y)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Perform aggregation of Water Use Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1985\n",
      "1990\n",
      "1995\n",
      "2000\n",
      "2005\n",
      "2010\n",
      "2015\n"
     ]
    }
   ],
   "source": [
    "dfs={}\n",
    "years=range(1985,2016,5)\n",
    "for y in years:\n",
    "    print(y)\n",
    "    dfs[y]=rdr(y,Uses,volUses,popUses)\n",
    "df=pd.concat(dfs,names=\"Year FIPS\".split()).swaplevel(0,1).sort_index()\n",
    "df=df.drop('nan',level=0)\n",
    "df[\"Population\"]=df[\"Population\"].apply(np.ceil).astype(int)\n",
    "df=calc_dependencies(df,Uses,years)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Present summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Population  DomesticSelfSupp  DomesticSelfPop  DomesticTotalUse  \\\n",
      "count  2.256500e+04      21877.000000     2.186900e+04      21772.000000   \n",
      "mean   8.798422e+04          0.001505     1.339608e+04          0.012256   \n",
      "std    2.878891e+05          0.002868     2.516093e+04          0.047704   \n",
      "min    0.000000e+00          0.000000    -1.073000e+04          0.000000   \n",
      "25%    1.104100e+04          0.000194     1.720000e+03          0.001341   \n",
      "50%    2.458000e+04          0.000636     5.813000e+03          0.002959   \n",
      "75%    6.006000e+04          0.001645     1.520000e+04          0.007522   \n",
      "max    1.017029e+07          0.104749     1.010120e+06          2.236696   \n",
      "\n",
      "       DomesticPubDeliv  PublicSupplyPop  PublicSupplyUse  IndustrialSelfSupp  \\\n",
      "count      21866.000000     2.186900e+04     22565.000000        22565.000000   \n",
      "mean           0.010756     7.424694e+04         0.017391            0.007769   \n",
      "std            0.046343     2.769585e+05         0.069957            0.050666   \n",
      "min            0.000000     0.000000e+00         0.000000            0.000000   \n",
      "25%            0.000816     6.470000e+03         0.001175            0.000000   \n",
      "50%            0.002019     1.605500e+04         0.003180            0.000041   \n",
      "75%            0.005627     4.313000e+04         0.009388            0.001466   \n",
      "max            2.131947     1.013998e+07         2.571207            2.026514   \n",
      "\n",
      "       IndustrialTotalUse  IrrigationTotalUse  IrrigationSurfaceUse  \\\n",
      "count        21866.000000        22565.000000          22565.000000   \n",
      "mean             0.014408            0.055515              0.033284   \n",
      "std              0.062923            0.217183              0.157381   \n",
      "min              0.000000            0.000000              0.000000   \n",
      "25%              0.000263            0.000097              0.000000   \n",
      "50%              0.001341            0.000885              0.000207   \n",
      "75%              0.006706            0.010550              0.002157   \n",
      "max              2.048830            4.825221              3.787339   \n",
      "\n",
      "       IrrigationGroundWUse  LivestockTotalUse  \n",
      "count          22565.000000       21217.000000  \n",
      "mean               0.022231           0.001101  \n",
      "std                0.094066           0.007325  \n",
      "min                0.000000           0.000000  \n",
      "25%                0.000000           0.000166  \n",
      "50%                0.000221           0.000498  \n",
      "75%                0.003540           0.001120  \n",
      "max                2.268021           0.924547  \n"
     ]
    }
   ],
   "source": [
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice negative values for DomesticSelfSupplied.  The DomesticSelfSuppliedPopulation variable is calcualted as the Total Population of County  - minus - the population served by public supply.  Below are the counties where the public served population exceeded the population of the whole county, and the total excess population across all records.  The saved dataset forces these populations to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FIPS   Year\n",
      "27033  2010       -3.0\n",
      "27081  2010       -4.0\n",
      "27117  2010       -4.0\n",
      "27173  2010       -2.0\n",
      "40063  1995      -10.0\n",
      "42101  2010       -4.0\n",
      "51680  2000   -10730.0\n",
      "72013  2000      -10.0\n",
      "72041  2000      -10.0\n",
      "72067  2000      -10.0\n",
      "72109  2000      -10.0\n",
      "72153  2000      -10.0\n",
      "Name: DomesticSelfPop, dtype: float64\n",
      "Total:  10807.0\n"
     ]
    }
   ],
   "source": [
    "print(df.loc[df[\"DomesticSelfPop\"]<0,\"DomesticSelfPop\"])\n",
    "print(\"Total: \",(-1*df.loc[df['DomesticSelfPop']<0,\"DomesticSelfPop\"]).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Population  DomesticSelfSupp  DomesticSelfPop  DomesticTotalUse  \\\n",
      "count  2.256500e+04      21877.000000     2.186900e+04      21772.000000   \n",
      "mean   8.798422e+04          0.001505     1.339657e+04          0.012256   \n",
      "std    2.878891e+05          0.002868     2.516057e+04          0.047704   \n",
      "min    0.000000e+00          0.000000     0.000000e+00          0.000000   \n",
      "25%    1.104100e+04          0.000194     1.720000e+03          0.001341   \n",
      "50%    2.458000e+04          0.000636     5.813000e+03          0.002959   \n",
      "75%    6.006000e+04          0.001645     1.520000e+04          0.007522   \n",
      "max    1.017029e+07          0.104749     1.010120e+06          2.236696   \n",
      "\n",
      "       DomesticPubDeliv  PublicSupplyPop  PublicSupplyUse  IndustrialSelfSupp  \\\n",
      "count      21866.000000     2.186900e+04     22565.000000        22565.000000   \n",
      "mean           0.010756     7.424694e+04         0.017391            0.007769   \n",
      "std            0.046343     2.769585e+05         0.069957            0.050666   \n",
      "min            0.000000     0.000000e+00         0.000000            0.000000   \n",
      "25%            0.000816     6.470000e+03         0.001175            0.000000   \n",
      "50%            0.002019     1.605500e+04         0.003180            0.000041   \n",
      "75%            0.005627     4.313000e+04         0.009388            0.001466   \n",
      "max            2.131947     1.013998e+07         2.571207            2.026514   \n",
      "\n",
      "       IndustrialTotalUse  IrrigationTotalUse  IrrigationSurfaceUse  \\\n",
      "count        21866.000000        22565.000000          22565.000000   \n",
      "mean             0.014408            0.055515              0.033284   \n",
      "std              0.062923            0.217183              0.157381   \n",
      "min              0.000000            0.000000              0.000000   \n",
      "25%              0.000263            0.000097              0.000000   \n",
      "50%              0.001341            0.000885              0.000207   \n",
      "75%              0.006706            0.010550              0.002157   \n",
      "max              2.048830            4.825221              3.787339   \n",
      "\n",
      "       IrrigationGroundWUse  LivestockTotalUse  \n",
      "count          22565.000000       21217.000000  \n",
      "mean               0.022231           0.001101  \n",
      "std                0.094066           0.007325  \n",
      "min                0.000000           0.000000  \n",
      "25%                0.000000           0.000166  \n",
      "50%                0.000221           0.000498  \n",
      "75%                0.003540           0.001120  \n",
      "max                2.268021           0.924547  \n"
     ]
    }
   ],
   "source": [
    "df[df<0]=0.0\n",
    "print(df.describe())\n",
    "df.to_csv(output_file_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example of reading resulting summary table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Population  DomesticSelfSupp  DomesticSelfPop  DomesticTotalUse  \\\n",
      "count  2.256500e+04      21877.000000     2.186900e+04      21772.000000   \n",
      "mean   8.798422e+04          0.001505     1.339657e+04          0.012256   \n",
      "std    2.878891e+05          0.002868     2.516057e+04          0.047704   \n",
      "min    0.000000e+00          0.000000     0.000000e+00          0.000000   \n",
      "25%    1.104100e+04          0.000194     1.720000e+03          0.001341   \n",
      "50%    2.458000e+04          0.000636     5.813000e+03          0.002959   \n",
      "75%    6.006000e+04          0.001645     1.520000e+04          0.007522   \n",
      "max    1.017029e+07          0.104749     1.010120e+06          2.236696   \n",
      "\n",
      "       DomesticPubDeliv  PublicSupplyPop  PublicSupplyUse  IndustrialSelfSupp  \\\n",
      "count      21866.000000     2.186900e+04     22565.000000        22565.000000   \n",
      "mean           0.010756     7.424694e+04         0.017391            0.007769   \n",
      "std            0.046343     2.769585e+05         0.069957            0.050666   \n",
      "min            0.000000     0.000000e+00         0.000000            0.000000   \n",
      "25%            0.000816     6.470000e+03         0.001175            0.000000   \n",
      "50%            0.002019     1.605500e+04         0.003180            0.000041   \n",
      "75%            0.005627     4.313000e+04         0.009388            0.001466   \n",
      "max            2.131947     1.013998e+07         2.571207            2.026514   \n",
      "\n",
      "       IndustrialTotalUse  IrrigationTotalUse  IrrigationSurfaceUse  \\\n",
      "count        21866.000000        22565.000000          22565.000000   \n",
      "mean             0.014408            0.055515              0.033284   \n",
      "std              0.062923            0.217183              0.157381   \n",
      "min              0.000000            0.000000              0.000000   \n",
      "25%              0.000263            0.000097              0.000000   \n",
      "50%              0.001341            0.000885              0.000207   \n",
      "75%              0.006706            0.010550              0.002157   \n",
      "max              2.048830            4.825221              3.787339   \n",
      "\n",
      "       IrrigationGroundWUse  LivestockTotalUse  \n",
      "count          22565.000000       21217.000000  \n",
      "mean               0.022231           0.001101  \n",
      "std                0.094066           0.007325  \n",
      "min                0.000000           0.000000  \n",
      "25%                0.000000           0.000166  \n",
      "50%                0.000221           0.000498  \n",
      "75%                0.003540           0.001120  \n",
      "max                2.268021           0.924547  \n",
      "MultiIndex([('01001', 1985),\n",
      "            ('01001', 1990),\n",
      "            ('01001', 1995),\n",
      "            ('01001', 2000),\n",
      "            ('01001', 2005),\n",
      "            ('01001', 2010),\n",
      "            ('01001', 2015),\n",
      "            ('01003', 1985),\n",
      "            ('01003', 1990),\n",
      "            ('01003', 1995),\n",
      "            ...\n",
      "            ('78020', 2000),\n",
      "            ('78020', 2005),\n",
      "            ('78020', 2010),\n",
      "            ('78020', 2015),\n",
      "            ('78030', 1985),\n",
      "            ('78030', 1990),\n",
      "            ('78030', 2000),\n",
      "            ('78030', 2005),\n",
      "            ('78030', 2010),\n",
      "            ('78030', 2015)],\n",
      "           names=['FIPS', 'Year'], length=22565)\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv(output_file_name,dtype={'FIPS':str},index_col=\"Year\") \n",
    "# Pandas will force recognition of FIPS as int() if asked to create multi-index from read_csv, so ...\n",
    "df.set_index('FIPS',append=True,inplace=True)\n",
    "df=df.reorder_levels((1,0))\n",
    "print(df.describe())\n",
    "print(df.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate derived quantities of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Population', 'DomesticSelfSupp', 'DomesticSelfPop', 'DomesticTotalUse',\n",
      "       'DomesticPubDeliv', 'PublicSupplyPop', 'PublicSupplyUse',\n",
      "       'IndustrialSelfSupp', 'IndustrialTotalUse', 'IrrigationTotalUse',\n",
      "       'IrrigationSurfaceUse', 'IrrigationGroundWUse', 'LivestockTotalUse',\n",
      "       'UrbanUse', 'AgUse', 'Domest_cap', 'Indust_cap', 'SW_ratio'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "df[\"UrbanUse\"]=df[\"DomesticTotalUse\"]+df[\"IndustrialTotalUse\"] # km3/year\n",
    "df[\"AgUse\"]=df[\"IrrigationTotalUse\"]+df[\"LivestockTotalUse\"] # km3/year\n",
    "df[\"Domest_cap\"]=1e9*df[\"DomesticTotalUse\"]/365.256/df[\"Population\"] # m3/day\n",
    "df[\"Indust_cap\"]=1e9*df[\"IndustrialTotalUse\"]/365.256/df[\"Population\"] # m3/day\n",
    "df[\"SW_ratio\"]=df[\"IrrigationSurfaceUse\"] / df[\"IrrigationTotalUse\"] # ratio\n",
    "print( df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Create  GeoJSON"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If fiona is not installed (which requires install of OGR from OSGEO) the remainder of this notebook is not for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import fiona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_data_json( df, year ):\n",
    "    lyr_name=\"usgs_county_water_{}\".format(year)\n",
    "    out_name=fldr+lyr_name+\".json\"\n",
    "    if os.path.exists(out_name):\n",
    "        # If you want the file re-written, mv it.\n",
    "        return\n",
    "    with fiona.open(county_fname,'r') as inShapefile:\n",
    "        source_crs=inShapefile.crs\n",
    "        schema=inShapefile.schema\n",
    "        schema[\"geometry\"]=\"MultiPolygon\"\n",
    "        new_attrs=OrderedDict([(x,\"float\") for x in df.columns])\n",
    "        schema[\"properties\"].update(new_attrs)\n",
    "        out_driver='GeoJSON'\n",
    "        with fiona.open(out_name,'w',driver=out_driver,crs=source_crs,schema=schema) as outData:\n",
    "            new_records=[]\n",
    "            for record in inShapefile.values():\n",
    "                try:\n",
    "                    for col in df.columns:\n",
    "                        record[\"properties\"][col]=df.loc[(record[\"properties\"][\"GEOID\"],year),col]\n",
    "                    if record[\"geometry\"][\"type\"] == \"Polygon\":\n",
    "                        record[\"geometry\"][\"type\"] = \"MultiPolygon\"\n",
    "                        record[\"geometry\"][\"coordinates\"]=[record[\"geometry\"][\"coordinates\"]]\n",
    "                    new_records.append(record)\n",
    "                except KeyError:\n",
    "                    print(record[\"properties\"][\"GEOID\"],\" not in USGS statistics.\")\n",
    "                    continue\n",
    "            outData.writerecords(new_records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for year in range(1985,2016,5):\n",
    "    create_data_json(df,year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot example data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the fraction of irrigation water derived from surface water sources (as opposed to groundwater) for CONUS.\n",
    "\n",
    "The fraction of surface water used for irrigation can be quite variable through time for each county.  For instance the difference between the ratio for each county can be $\\pm$100%, and has a fairly high standard deviation (see below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    17723.000000\n",
      "mean         0.000202\n",
      "std          0.441333\n",
      "min         -1.000000\n",
      "25%         -0.186488\n",
      "50%          0.000000\n",
      "75%          0.189979\n",
      "max          1.000000\n",
      "Name: SW_ratio, dtype: float64\n",
      "count    2897.000000\n",
      "mean        0.202664\n",
      "std         0.170059\n",
      "min         0.000000\n",
      "25%         0.068402\n",
      "50%         0.166760\n",
      "75%         0.303292\n",
      "max         1.272792\n",
      "Name: SW_ratio, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"SW_ratio\"].groupby(level=1).diff(1).describe()) # \n",
    "print(df[\"SW_ratio\"].groupby(level=1).diff(1).std(level=0).describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While a reasonable hypothesis is that this temporal variability can be explained by drought conditions (irrigators switch to greater groundwater withdrawals when surface water is unavailable), we do not test this, but instead assume that over the climatological record between 1985 and 2015, the mean ratio of surface to total irrigation water may be a more appropriate representation of typical county-wide irrigation water sourcing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sw_ratio = df[\"SW_ratio\"].mean(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import cartopy\n",
    "import shapely.geometry as geometry\n",
    "# Please note: shapely must be installed from source and proper integration with GEOS ensured.\n",
    "#              see https://pypi.org/project/Shapely/\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = 2015\n",
    "county_geoids = [(geometry.MultiPolygon(geometry.shape(pol['geometry'])),pol['properties']['GEOID']) for pol in fiona.open(fldr+\"usgs_county_water_{}.json\".format(year))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 684x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap=plt.get_cmap('magma')\n",
    "fig = plt.figure(figsize=(9.5,6))\n",
    "globe = cartopy.crs.Globe(semimajor_axis=6371000.)\n",
    "proj = cartopy.crs.LambertConformal(standard_parallels=[25.0], globe=globe)\n",
    "\n",
    "ax = fig.add_subplot(1,1,1,projection=proj)\n",
    "ax.set_extent((-125., -70., 21., 54.))  # Conterminous USA\n",
    "year = 2015\n",
    "\n",
    "for county_geoid in county_geoids:\n",
    "    color = cmap(sw_ratio.loc[county_geoid[1]])\n",
    "    ax.add_geometries(county_geoid[0],cartopy.crs.PlateCarree(),facecolor=color,edgecolor='k',linewidth=0.05)\n",
    "cax = fig.add_axes([0.3, 0.83, 0.4, 0.02])\n",
    "norm = mpl.colors.Normalize(vmin=0, vmax=1)\n",
    "cb = mpl.colorbar.ColorbarBase(cax, cmap=cmap, norm=norm, orientation='horizontal')\n",
    "cb.set_label('Fraction of Surface sourced Irrigation water', fontsize=10)\n",
    "cb.set_ticks(np.arange(0, 1.01, 0.25))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}