├── README.md ├── data ├── csv │ ├── ACS_MD_15_5YR_DP03.csv │ ├── ACS_MD_15_5YR_DP05.csv │ ├── cgr-definitions-table.csv │ ├── cgr-premiums-table.csv │ └── territory-definitions-table.csv └── pdf │ ├── cgr-definitions-table.pdf │ ├── cgr-premiums-table.pdf │ └── territory-definitions-table.pdf ├── py └── table-extractor.ipynb ├── reference ├── cgr-premiums-table-schema.pdf └── premium-calculation-sheet.pdf ├── requirements.txt └── rmd └── analysis.Rmd /README.md: -------------------------------------------------------------------------------- 1 | # How We Analyzed Allstate’s Car Insurance Algorithm 2 | 3 | This repository contains code to reproduce the findings featured in our story, ["Suckers List: How Allstate’s Secret Auto Insurance Algorithm Squeezes Big Spenders."](https://themarkup.org/allstates-algorithm/2020/02/25/car-insurance-suckers-list) 4 | 5 | Our methodology is described in ["How We Analyzed Allstate’s Car Insurance Algorithm."](https://themarkup.org/allstates-algorithm/2020/02/25/show-your-work-car-insurance-suckers-list) 6 | 7 | The data for this analysis can be found in the `data` folder. 8 | 9 | The code for all of the analysis is available in `rmd/allstate-tree-analysis.Rmd`. You will likely need to edit the paths for the `.csv` files. 10 | 11 | The code to clean and process the Allstate pdf tables is in the Jupyter Notebook `py/table-extractor.ipynb`. The code for this has already been run. 12 | 13 | ## Installation 14 | 15 | ### PDF Extractor 16 | 17 | We downloaded the tables in pdf form from the [Maryland SERFF Filing Database](https://filingaccess.serff.com/sfa/home/MD) and turned them into csv files using Python 3.7.3 with a Jupyter notebook, `py/table-extractor.ipynb`. 18 | 19 | We included the cleaned csv files in the folder `data/csv`. 20 | 21 | Running this code is optional, since the cleaned csv files are provided. 22 | 23 | To run, you must download [tika](https://tika.apache.org/1.10/gettingstarted.html), which is used to convert the pdf files to xml. If you use [brew](https://formulae.brew.sh/formula/tika), you can simply run `brew install tika`. This notebook has been tested on OS X, installation may vary depending on your operating system. 24 | 25 | You must also install the appropriate packages by running `pip -r requirements.txt`. 26 | 27 | ### Analysis 28 | 29 | We merged and analyzed the data using R version 3.6.1 (2019-07-05). To run our code, open `rmd/analysis.Rmd`. We recommend using [RStudio](https://rstudio.com/) for viewing. 30 | 31 | ## Data 32 | 33 | ### CGR Premiums Table 34 | 35 | This table, found in ALSE-129270805, contains individual pricing data. The table, as we recieved it via public records, can be found in `data/pdf/cgr-premiums-table.pdf`. The cleaned csv file is `data/csv/cgr-premiums-table.csv`. 36 | 37 | Allstate provided a data dictionary to Maryland regulators, it is available in `reference/cgr-premiums-table-schema.pdf`. 38 | 39 | The column names we use and the titles they are given in the schema are included below: 40 | 41 | | Column | Description | 42 | |--------|-------------| 43 | |`territory`| (1a) Rating Territory| 44 | |`gender`| (1b) Gender of Oldest Operator| 45 | |`birthdate`| (1c) Birthdate of Oldest Operator| 46 | |`ypc`| (1d) Years with Prior Carrier| 47 | |`current_premium`| (2) Current Premium| 48 | |`indicated_premium`| (3) Indicated Premium| 49 | |`selected_premium`| (4) Selected Premium| 50 | |`underlying_premium`| (5a) Underlying Rating Plan Proposed CGR Applicable Coverage Premium| 51 | |`fixed_expenses`| (5b) Proposed Fixed Expense and Other Premium | 52 | |`underlying_total_premium`|(5c) Underlying Rating Plan Proposed Total Premium| 53 | |`cgr_factor`| (6) Selected Group Factor| 54 | |`cgr`| (7) Complementary Group| 55 | 56 | ### Territory Definitions Table 57 | 58 | This table, found in ALSE-129270805, defines the "territories", which are referred to in the CGR Premiums Table. The table, as we recieved it via public records, can be found in `data/pdf/territory-definitions-table.pdf`. The cleaned csv file is `data/csv/territory-definitions-table.csv`. 59 | 60 | The column names we use and the column names in the filing documentation are included below: 61 | 62 | | Column | Description | 63 | |--------|-------------| 64 | | `county`| County 65 | |`county_code`| County Code 66 | |`territory`| Territory 67 | |`zipcode`| Zip Code 68 | |`town`| Town 69 | |`area`| Area 70 | 71 | 72 | ### CGR Definition Table 73 | 74 | This table, found in ALSE-129270805, defines the "Complementary Groups". The table, as we recieved it via public records, can be found in `data/pdf/cgr-definitions-table.pdf`. The cleaned csv file is `data/csv/cgr-definitions-table.csv`. 75 | 76 | The column names we use and the column names in the filing documentation are included below: 77 | 78 | | Column | Description | 79 | |--------|-------------| 80 | | `cgr` | Complementary Group| 81 | | `aa` | AA | 82 | | `bb` | BB | 83 | | `cc` | CC | 84 | | `va` | VA | 85 | | `dd` | DD | 86 | | `hh` | HH | 87 | | `ss` | SS | 88 | 89 | The column names correspond to those found in the worksheet used to calculate a policyholder's premium, see `reference/premium-calcluation-sheet.pdf` for one included in ALSE-129270805. 90 | 91 | ### US Census 2011-2015 American Community Survey 5-Year Estimates Data 92 | 93 | We used ZCTA-level data from US Census 2011-2015 American Community Survey 5-Year Estimates to do race and income analyses. We include a copy of "DP03 - Selected Economic Characteristics" (`data/csv/ACS_MD_15_5YR_DP03.csv`) and "DP05 - ACS Demographic and Housing Estimates" (`data/csv/ACS_MD_15_5YR_DP05.csv`) for ZCTA codes fully or partially in Maryland. 94 | 95 | ## Licensing 96 | 97 | Copyright 2020, The Markup News Inc. 98 | 99 | Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: 100 | 101 | The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 102 | 103 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. -------------------------------------------------------------------------------- /data/csv/cgr-definitions-table.csv: -------------------------------------------------------------------------------- 1 | cgr,aa,bb,cc,va,dd,hh,ss 2 | 1OW,0.1066,0.1066,0.1066,0.1066,0.1066,0.1066,0.1066 3 | 7HB,0.1071,0.1071,0.1071,0.1071,0.1071,0.1071,0.1071 4 | 1OE,0.1076,0.1076,0.1076,0.1076,0.1076,0.1076,0.1076 5 | 1OD,0.1082,0.1082,0.1082,0.1082,0.1082,0.1082,0.1082 6 | I9Y,0.1087,0.1087,0.1087,0.1087,0.1087,0.1087,0.1087 7 | NYF,0.1093,0.1093,0.1093,0.1093,0.1093,0.1093,0.1093 8 | SN8,0.1098,0.1098,0.1098,0.1098,0.1098,0.1098,0.1098 9 | KYM,0.1104,0.1104,0.1104,0.1104,0.1104,0.1104,0.1104 10 | SPM,0.1109,0.1109,0.1109,0.1109,0.1109,0.1109,0.1109 11 | SPD,0.1115,0.1115,0.1115,0.1115,0.1115,0.1115,0.1115 12 | PKG,0.1120,0.1120,0.1120,0.1120,0.1120,0.1120,0.1120 13 | BW3,0.1126,0.1126,0.1126,0.1126,0.1126,0.1126,0.1126 14 | 34M,0.1131,0.1131,0.1131,0.1131,0.1131,0.1131,0.1131 15 | SP2,0.1137,0.1137,0.1137,0.1137,0.1137,0.1137,0.1137 16 | XPF,0.1143,0.1143,0.1143,0.1143,0.1143,0.1143,0.1143 17 | IXS,0.1148,0.1148,0.1148,0.1148,0.1148,0.1148,0.1148 18 | Y17,0.1154,0.1154,0.1154,0.1154,0.1154,0.1154,0.1154 19 | IXQ,0.1160,0.1160,0.1160,0.1160,0.1160,0.1160,0.1160 20 | XPV,0.1166,0.1166,0.1166,0.1166,0.1166,0.1166,0.1166 21 | KRQ,0.1172,0.1172,0.1172,0.1172,0.1172,0.1172,0.1172 22 | NY8,0.1177,0.1177,0.1177,0.1177,0.1177,0.1177,0.1177 23 | Z6O,0.1183,0.1183,0.1183,0.1183,0.1183,0.1183,0.1183 24 | JNT,0.1189,0.1189,0.1189,0.1189,0.1189,0.1189,0.1189 25 | NY6,0.1195,0.1195,0.1195,0.1195,0.1195,0.1195,0.1195 26 | QE1,0.1201,0.1201,0.1201,0.1201,0.1201,0.1201,0.1201 27 | PQR,0.1207,0.1207,0.1207,0.1207,0.1207,0.1207,0.1207 28 | PQT,0.1213,0.1213,0.1213,0.1213,0.1213,0.1213,0.1213 29 | I96,0.1219,0.1219,0.1219,0.1219,0.1219,0.1219,0.1219 30 | L7D,0.1225,0.1225,0.1225,0.1225,0.1225,0.1225,0.1225 31 | AH7,0.1231,0.1231,0.1231,0.1231,0.1231,0.1231,0.1231 32 | X48,0.1237,0.1237,0.1237,0.1237,0.1237,0.1237,0.1237 33 | BES,0.1244,0.1244,0.1244,0.1244,0.1244,0.1244,0.1244 34 | X6H,0.1250,0.1250,0.1250,0.1250,0.1250,0.1250,0.1250 35 | T8S,0.1256,0.1256,0.1256,0.1256,0.1256,0.1256,0.1256 36 | LSV,0.1262,0.1262,0.1262,0.1262,0.1262,0.1262,0.1262 37 | 10X,0.1269,0.1269,0.1269,0.1269,0.1269,0.1269,0.1269 38 | 0Q6,0.1275,0.1275,0.1275,0.1275,0.1275,0.1275,0.1275 39 | 1DS,0.1281,0.1281,0.1281,0.1281,0.1281,0.1281,0.1281 40 | VLP,0.1288,0.1288,0.1288,0.1288,0.1288,0.1288,0.1288 41 | GRD,0.1294,0.1294,0.1294,0.1294,0.1294,0.1294,0.1294 42 | XMV,0.1301,0.1301,0.1301,0.1301,0.1301,0.1301,0.1301 43 | A5D,0.1307,0.1307,0.1307,0.1307,0.1307,0.1307,0.1307 44 | TJH,0.1314,0.1314,0.1314,0.1314,0.1314,0.1314,0.1314 45 | KRA,0.1320,0.1320,0.1320,0.1320,0.1320,0.1320,0.1320 46 | KKS,0.1327,0.1327,0.1327,0.1327,0.1327,0.1327,0.1327 47 | KKH,0.1333,0.1333,0.1333,0.1333,0.1333,0.1333,0.1333 48 | K1P,0.1340,0.1340,0.1340,0.1340,0.1340,0.1340,0.1340 49 | NOW,0.1347,0.1347,0.1347,0.1347,0.1347,0.1347,0.1347 50 | KKC,0.1353,0.1353,0.1353,0.1353,0.1353,0.1353,0.1353 51 | 4TY,0.1360,0.1360,0.1360,0.1360,0.1360,0.1360,0.1360 52 | OR4,0.1367,0.1367,0.1367,0.1367,0.1367,0.1367,0.1367 53 | VU4,0.1374,0.1374,0.1374,0.1374,0.1374,0.1374,0.1374 54 | S87,0.1381,0.1381,0.1381,0.1381,0.1381,0.1381,0.1381 55 | K1K,0.1387,0.1387,0.1387,0.1387,0.1387,0.1387,0.1387 56 | E26,0.1394,0.1394,0.1394,0.1394,0.1394,0.1394,0.1394 57 | K1I,0.1401,0.1401,0.1401,0.1401,0.1401,0.1401,0.1401 58 | Q9H,0.1408,0.1408,0.1408,0.1408,0.1408,0.1408,0.1408 59 | EIS,0.1415,0.1415,0.1415,0.1415,0.1415,0.1415,0.1415 60 | CT4,0.1422,0.1422,0.1422,0.1422,0.1422,0.1422,0.1422 61 | RLO,0.1429,0.1429,0.1429,0.1429,0.1429,0.1429,0.1429 62 | O0M,0.1437,0.1437,0.1437,0.1437,0.1437,0.1437,0.1437 63 | 89Y,0.1444,0.1444,0.1444,0.1444,0.1444,0.1444,0.1444 64 | VK7,0.1451,0.1451,0.1451,0.1451,0.1451,0.1451,0.1451 65 | LMK,0.1458,0.1458,0.1458,0.1458,0.1458,0.1458,0.1458 66 | 70U,0.1465,0.1465,0.1465,0.1465,0.1465,0.1465,0.1465 67 | 05T,0.1473,0.1473,0.1473,0.1473,0.1473,0.1473,0.1473 68 | RLT,0.1480,0.1480,0.1480,0.1480,0.1480,0.1480,0.1480 69 | ORB,0.1487,0.1487,0.1487,0.1487,0.1487,0.1487,0.1487 70 | 00T,0.1495,0.1495,0.1495,0.1495,0.1495,0.1495,0.1495 71 | DPG,0.1502,0.1502,0.1502,0.1502,0.1502,0.1502,0.1502 72 | FX4,0.1510,0.1510,0.1510,0.1510,0.1510,0.1510,0.1510 73 | N44,0.1517,0.1517,0.1517,0.1517,0.1517,0.1517,0.1517 74 | R56,0.1525,0.1525,0.1525,0.1525,0.1525,0.1525,0.1525 75 | ONE,0.1533,0.1533,0.1533,0.1533,0.1533,0.1533,0.1533 76 | ONS,0.1540,0.1540,0.1540,0.1540,0.1540,0.1540,0.1540 77 | 9AG,0.1548,0.1548,0.1548,0.1548,0.1548,0.1548,0.1548 78 | ONU,0.1556,0.1556,0.1556,0.1556,0.1556,0.1556,0.1556 79 | V0V,0.1563,0.1563,0.1563,0.1563,0.1563,0.1563,0.1563 80 | V9L,0.1571,0.1571,0.1571,0.1571,0.1571,0.1571,0.1571 81 | RZI,0.1579,0.1579,0.1579,0.1579,0.1579,0.1579,0.1579 82 | FXO,0.1587,0.1587,0.1587,0.1587,0.1587,0.1587,0.1587 83 | 8CW,0.1595,0.1595,0.1595,0.1595,0.1595,0.1595,0.1595 84 | 8CU,0.1603,0.1603,0.1603,0.1603,0.1603,0.1603,0.1603 85 | OWR,0.1611,0.1611,0.1611,0.1611,0.1611,0.1611,0.1611 86 | G37,0.1619,0.1619,0.1619,0.1619,0.1619,0.1619,0.1619 87 | 2LQ,0.1627,0.1627,0.1627,0.1627,0.1627,0.1627,0.1627 88 | DP1,0.1635,0.1635,0.1635,0.1635,0.1635,0.1635,0.1635 89 | 621,0.1643,0.1643,0.1643,0.1643,0.1643,0.1643,0.1643 90 | 6CG,0.1651,0.1651,0.1651,0.1651,0.1651,0.1651,0.1651 91 | MR6,0.1660,0.1660,0.1660,0.1660,0.1660,0.1660,0.1660 92 | 3RL,0.1668,0.1668,0.1668,0.1668,0.1668,0.1668,0.1668 93 | CH1,0.1676,0.1676,0.1676,0.1676,0.1676,0.1676,0.1676 94 | LVR,0.1685,0.1685,0.1685,0.1685,0.1685,0.1685,0.1685 95 | L02,0.1693,0.1693,0.1693,0.1693,0.1693,0.1693,0.1693 96 | 0NA,0.1701,0.1701,0.1701,0.1701,0.1701,0.1701,0.1701 97 | MRF,0.1710,0.1710,0.1710,0.1710,0.1710,0.1710,0.1710 98 | Z74,0.1718,0.1718,0.1718,0.1718,0.1718,0.1718,0.1718 99 | SA6,0.1727,0.1727,0.1727,0.1727,0.1727,0.1727,0.1727 100 | UYQ,0.1736,0.1736,0.1736,0.1736,0.1736,0.1736,0.1736 101 | APR,0.1744,0.1744,0.1744,0.1744,0.1744,0.1744,0.1744 102 | M5E,0.1753,0.1753,0.1753,0.1753,0.1753,0.1753,0.1753 103 | XQ1,0.1762,0.1762,0.1762,0.1762,0.1762,0.1762,0.1762 104 | 7YK,0.1770,0.1770,0.1770,0.1770,0.1770,0.1770,0.1770 105 | VKW,0.1779,0.1779,0.1779,0.1779,0.1779,0.1779,0.1779 106 | 87K,0.1788,0.1788,0.1788,0.1788,0.1788,0.1788,0.1788 107 | Y05,0.1797,0.1797,0.1797,0.1797,0.1797,0.1797,0.1797 108 | K7M,0.1806,0.1806,0.1806,0.1806,0.1806,0.1806,0.1806 109 | HY5,0.1815,0.1815,0.1815,0.1815,0.1815,0.1815,0.1815 110 | 24C,0.1824,0.1824,0.1824,0.1824,0.1824,0.1824,0.1824 111 | 4MH,0.1833,0.1833,0.1833,0.1833,0.1833,0.1833,0.1833 112 | MAC,0.1842,0.1842,0.1842,0.1842,0.1842,0.1842,0.1842 113 | B33,0.1852,0.1852,0.1852,0.1852,0.1852,0.1852,0.1852 114 | HM4,0.1861,0.1861,0.1861,0.1861,0.1861,0.1861,0.1861 115 | AHU,0.1870,0.1870,0.1870,0.1870,0.1870,0.1870,0.1870 116 | AFA,0.1879,0.1879,0.1879,0.1879,0.1879,0.1879,0.1879 117 | GCO,0.1889,0.1889,0.1889,0.1889,0.1889,0.1889,0.1889 118 | CCJ,0.1898,0.1898,0.1898,0.1898,0.1898,0.1898,0.1898 119 | 39D,0.1908,0.1908,0.1908,0.1908,0.1908,0.1908,0.1908 120 | KXC,0.1917,0.1917,0.1917,0.1917,0.1917,0.1917,0.1917 121 | KXE,0.1927,0.1927,0.1927,0.1927,0.1927,0.1927,0.1927 122 | Y0B,0.1936,0.1936,0.1936,0.1936,0.1936,0.1936,0.1936 123 | 67P,0.1946,0.1946,0.1946,0.1946,0.1946,0.1946,0.1946 124 | XCU,0.1956,0.1956,0.1956,0.1956,0.1956,0.1956,0.1956 125 | 2LI,0.1965,0.1965,0.1965,0.1965,0.1965,0.1965,0.1965 126 | 1RR,0.1975,0.1975,0.1975,0.1975,0.1975,0.1975,0.1975 127 | 1RZ,0.1985,0.1985,0.1985,0.1985,0.1985,0.1985,0.1985 128 | SEG,0.1995,0.1995,0.1995,0.1995,0.1995,0.1995,0.1995 129 | NT0,0.2005,0.2005,0.2005,0.2005,0.2005,0.2005,0.2005 130 | Q8J,0.2015,0.2015,0.2015,0.2015,0.2015,0.2015,0.2015 131 | D6P,0.2025,0.2025,0.2025,0.2025,0.2025,0.2025,0.2025 132 | 5PX,0.2035,0.2035,0.2035,0.2035,0.2035,0.2035,0.2035 133 | ZPM,0.2045,0.2045,0.2045,0.2045,0.2045,0.2045,0.2045 134 | 06N,0.2055,0.2055,0.2055,0.2055,0.2055,0.2055,0.2055 135 | RM3,0.2066,0.2066,0.2066,0.2066,0.2066,0.2066,0.2066 136 | WMM,0.2076,0.2076,0.2076,0.2076,0.2076,0.2076,0.2076 137 | YAX,0.2086,0.2086,0.2086,0.2086,0.2086,0.2086,0.2086 138 | KJE,0.2097,0.2097,0.2097,0.2097,0.2097,0.2097,0.2097 139 | BBB,0.2107,0.2107,0.2107,0.2107,0.2107,0.2107,0.2107 140 | P7A,0.2118,0.2118,0.2118,0.2118,0.2118,0.2118,0.2118 141 | WMA,0.2128,0.2128,0.2128,0.2128,0.2128,0.2128,0.2128 142 | KJT,0.2139,0.2139,0.2139,0.2139,0.2139,0.2139,0.2139 143 | 6GV,0.2150,0.2150,0.2150,0.2150,0.2150,0.2150,0.2150 144 | TUZ,0.2160,0.2160,0.2160,0.2160,0.2160,0.2160,0.2160 145 | FR8,0.2171,0.2171,0.2171,0.2171,0.2171,0.2171,0.2171 146 | VMU,0.2182,0.2182,0.2182,0.2182,0.2182,0.2182,0.2182 147 | TUD,0.2193,0.2193,0.2193,0.2193,0.2193,0.2193,0.2193 148 | H2C,0.2204,0.2204,0.2204,0.2204,0.2204,0.2204,0.2204 149 | 5IE,0.2215,0.2215,0.2215,0.2215,0.2215,0.2215,0.2215 150 | E7N,0.2226,0.2226,0.2226,0.2226,0.2226,0.2226,0.2226 151 | 8DW,0.2237,0.2237,0.2237,0.2237,0.2237,0.2237,0.2237 152 | WK5,0.2248,0.2248,0.2248,0.2248,0.2248,0.2248,0.2248 153 | R94,0.2259,0.2259,0.2259,0.2259,0.2259,0.2259,0.2259 154 | CW9,0.2270,0.2270,0.2270,0.2270,0.2270,0.2270,0.2270 155 | O05,0.2282,0.2282,0.2282,0.2282,0.2282,0.2282,0.2282 156 | CW4,0.2293,0.2293,0.2293,0.2293,0.2293,0.2293,0.2293 157 | CYD,0.2305,0.2305,0.2305,0.2305,0.2305,0.2305,0.2305 158 | O0Q,0.2316,0.2316,0.2316,0.2316,0.2316,0.2316,0.2316 159 | CY5,0.2328,0.2328,0.2328,0.2328,0.2328,0.2328,0.2328 160 | PO7,0.2339,0.2339,0.2339,0.2339,0.2339,0.2339,0.2339 161 | W4Z,0.2351,0.2351,0.2351,0.2351,0.2351,0.2351,0.2351 162 | G9S,0.2363,0.2363,0.2363,0.2363,0.2363,0.2363,0.2363 163 | 802,0.2374,0.2374,0.2374,0.2374,0.2374,0.2374,0.2374 164 | VCX,0.2386,0.2386,0.2386,0.2386,0.2386,0.2386,0.2386 165 | RQQ,0.2398,0.2398,0.2398,0.2398,0.2398,0.2398,0.2398 166 | CWB,0.2410,0.2410,0.2410,0.2410,0.2410,0.2410,0.2410 167 | FKT,0.2422,0.2422,0.2422,0.2422,0.2422,0.2422,0.2422 168 | 5OY,0.2434,0.2434,0.2434,0.2434,0.2434,0.2434,0.2434 169 | 4UF,0.2446,0.2446,0.2446,0.2446,0.2446,0.2446,0.2446 170 | 4UG,0.2459,0.2459,0.2459,0.2459,0.2459,0.2459,0.2459 171 | E1P,0.2471,0.2471,0.2471,0.2471,0.2471,0.2471,0.2471 172 | CLY,0.2483,0.2483,0.2483,0.2483,0.2483,0.2483,0.2483 173 | CKE,0.2496,0.2496,0.2496,0.2496,0.2496,0.2496,0.2496 174 | IH8,0.2508,0.2508,0.2508,0.2508,0.2508,0.2508,0.2508 175 | 3CI,0.2521,0.2521,0.2521,0.2521,0.2521,0.2521,0.2521 176 | PAX,0.2533,0.2533,0.2533,0.2533,0.2533,0.2533,0.2533 177 | ZFT,0.2546,0.2546,0.2546,0.2546,0.2546,0.2546,0.2546 178 | 0OH,0.2558,0.2558,0.2558,0.2558,0.2558,0.2558,0.2558 179 | 8NC,0.2571,0.2571,0.2571,0.2571,0.2571,0.2571,0.2571 180 | 8NG,0.2584,0.2584,0.2584,0.2584,0.2584,0.2584,0.2584 181 | 5W7,0.2597,0.2597,0.2597,0.2597,0.2597,0.2597,0.2597 182 | 8NH,0.2610,0.2610,0.2610,0.2610,0.2610,0.2610,0.2610 183 | M4T,0.2623,0.2623,0.2623,0.2623,0.2623,0.2623,0.2623 184 | UXO,0.2636,0.2636,0.2636,0.2636,0.2636,0.2636,0.2636 185 | AWE,0.2649,0.2649,0.2649,0.2649,0.2649,0.2649,0.2649 186 | MQZ,0.2662,0.2662,0.2662,0.2662,0.2662,0.2662,0.2662 187 | 7XJ,0.2676,0.2676,0.2676,0.2676,0.2676,0.2676,0.2676 188 | ZXC,0.2689,0.2689,0.2689,0.2689,0.2689,0.2689,0.2689 189 | L9V,0.2702,0.2702,0.2702,0.2702,0.2702,0.2702,0.2702 190 | AW8,0.2716,0.2716,0.2716,0.2716,0.2716,0.2716,0.2716 191 | UX8,0.2729,0.2729,0.2729,0.2729,0.2729,0.2729,0.2729 192 | VLQ,0.2743,0.2743,0.2743,0.2743,0.2743,0.2743,0.2743 193 | 54P,0.2757,0.2757,0.2757,0.2757,0.2757,0.2757,0.2757 194 | DQC,0.2770,0.2770,0.2770,0.2770,0.2770,0.2770,0.2770 195 | 54Z,0.2784,0.2784,0.2784,0.2784,0.2784,0.2784,0.2784 196 | ZLL,0.2798,0.2798,0.2798,0.2798,0.2798,0.2798,0.2798 197 | ZLN,0.2812,0.2812,0.2812,0.2812,0.2812,0.2812,0.2812 198 | F2L,0.2826,0.2826,0.2826,0.2826,0.2826,0.2826,0.2826 199 | CK5,0.2840,0.2840,0.2840,0.2840,0.2840,0.2840,0.2840 200 | MWD,0.2854,0.2854,0.2854,0.2854,0.2854,0.2854,0.2854 201 | JL6,0.2869,0.2869,0.2869,0.2869,0.2869,0.2869,0.2869 202 | 7JI,0.2883,0.2883,0.2883,0.2883,0.2883,0.2883,0.2883 203 | DMH,0.2897,0.2897,0.2897,0.2897,0.2897,0.2897,0.2897 204 | 1MR,0.2912,0.2912,0.2912,0.2912,0.2912,0.2912,0.2912 205 | L10,0.2926,0.2926,0.2926,0.2926,0.2926,0.2926,0.2926 206 | UC3,0.2941,0.2941,0.2941,0.2941,0.2941,0.2941,0.2941 207 | AEG,0.2956,0.2956,0.2956,0.2956,0.2956,0.2956,0.2956 208 | B0S,0.2970,0.2970,0.2970,0.2970,0.2970,0.2970,0.2970 209 | B0R,0.2985,0.2985,0.2985,0.2985,0.2985,0.2985,0.2985 210 | 36L,0.3000,0.3000,0.3000,0.3000,0.3000,0.3000,0.3000 211 | 36C,0.3015,0.3015,0.3015,0.3015,0.3015,0.3015,0.3015 212 | 381,0.3030,0.3030,0.3030,0.3030,0.3030,0.3030,0.3030 213 | B04,0.3045,0.3045,0.3045,0.3045,0.3045,0.3045,0.3045 214 | DM6,0.3060,0.3060,0.3060,0.3060,0.3060,0.3060,0.3060 215 | MOG,0.3076,0.3076,0.3076,0.3076,0.3076,0.3076,0.3076 216 | PST,0.3091,0.3091,0.3091,0.3091,0.3091,0.3091,0.3091 217 | 7RD,0.3106,0.3106,0.3106,0.3106,0.3106,0.3106,0.3106 218 | NM4,0.3122,0.3122,0.3122,0.3122,0.3122,0.3122,0.3122 219 | BCI,0.3137,0.3137,0.3137,0.3137,0.3137,0.3137,0.3137 220 | NM1,0.3153,0.3153,0.3153,0.3153,0.3153,0.3153,0.3153 221 | X61,0.3169,0.3169,0.3169,0.3169,0.3169,0.3169,0.3169 222 | KMI,0.3185,0.3185,0.3185,0.3185,0.3185,0.3185,0.3185 223 | 253,0.3200,0.3200,0.3200,0.3200,0.3200,0.3200,0.3200 224 | TD0,0.3216,0.3216,0.3216,0.3216,0.3216,0.3216,0.3216 225 | ETL,0.3232,0.3232,0.3232,0.3232,0.3232,0.3232,0.3232 226 | TVQ,0.3249,0.3249,0.3249,0.3249,0.3249,0.3249,0.3249 227 | S16,0.3265,0.3265,0.3265,0.3265,0.3265,0.3265,0.3265 228 | TVC,0.3281,0.3281,0.3281,0.3281,0.3281,0.3281,0.3281 229 | JZ0,0.3297,0.3297,0.3297,0.3297,0.3297,0.3297,0.3297 230 | K6F,0.3314,0.3314,0.3314,0.3314,0.3314,0.3314,0.3314 231 | 2Q1,0.3330,0.3330,0.3330,0.3330,0.3330,0.3330,0.3330 232 | SRH,0.3347,0.3347,0.3347,0.3347,0.3347,0.3347,0.3347 233 | SRS,0.3364,0.3364,0.3364,0.3364,0.3364,0.3364,0.3364 234 | JZV,0.3380,0.3380,0.3380,0.3380,0.3380,0.3380,0.3380 235 | NMT,0.3397,0.3397,0.3397,0.3397,0.3397,0.3397,0.3397 236 | NMR,0.3414,0.3414,0.3414,0.3414,0.3414,0.3414,0.3414 237 | T2X,0.3431,0.3431,0.3431,0.3431,0.3431,0.3431,0.3431 238 | NMX,0.3448,0.3448,0.3448,0.3448,0.3448,0.3448,0.3448 239 | HN0,0.3466,0.3466,0.3466,0.3466,0.3466,0.3466,0.3466 240 | HN7,0.3483,0.3483,0.3483,0.3483,0.3483,0.3483,0.3483 241 | B8N,0.3500,0.3500,0.3500,0.3500,0.3500,0.3500,0.3500 242 | NWL,0.3518,0.3518,0.3518,0.3518,0.3518,0.3518,0.3518 243 | NW5,0.3535,0.3535,0.3535,0.3535,0.3535,0.3535,0.3535 244 | 80N,0.3553,0.3553,0.3553,0.3553,0.3553,0.3553,0.3553 245 | 2QE,0.3571,0.3571,0.3571,0.3571,0.3571,0.3571,0.3571 246 | FHB,0.3588,0.3588,0.3588,0.3588,0.3588,0.3588,0.3588 247 | 4VH,0.3606,0.3606,0.3606,0.3606,0.3606,0.3606,0.3606 248 | 2QH,0.3624,0.3624,0.3624,0.3624,0.3624,0.3624,0.3624 249 | IVF,0.3642,0.3642,0.3642,0.3642,0.3642,0.3642,0.3642 250 | EB8,0.3661,0.3661,0.3661,0.3661,0.3661,0.3661,0.3661 251 | FHR,0.3679,0.3679,0.3679,0.3679,0.3679,0.3679,0.3679 252 | 8U4,0.3697,0.3697,0.3697,0.3697,0.3697,0.3697,0.3697 253 | E0W,0.3716,0.3716,0.3716,0.3716,0.3716,0.3716,0.3716 254 | UB7,0.3734,0.3734,0.3734,0.3734,0.3734,0.3734,0.3734 255 | 1Q2,0.3753,0.3753,0.3753,0.3753,0.3753,0.3753,0.3753 256 | 5LV,0.3771,0.3771,0.3771,0.3771,0.3771,0.3771,0.3771 257 | 1QE,0.3790,0.3790,0.3790,0.3790,0.3790,0.3790,0.3790 258 | TIR,0.3809,0.3809,0.3809,0.3809,0.3809,0.3809,0.3809 259 | L90,0.3828,0.3828,0.3828,0.3828,0.3828,0.3828,0.3828 260 | Q4F,0.3847,0.3847,0.3847,0.3847,0.3847,0.3847,0.3847 261 | W73,0.3866,0.3866,0.3866,0.3866,0.3866,0.3866,0.3866 262 | CPY,0.3886,0.3886,0.3886,0.3886,0.3886,0.3886,0.3886 263 | RJZ,0.3905,0.3905,0.3905,0.3905,0.3905,0.3905,0.3905 264 | V7O,0.3925,0.3925,0.3925,0.3925,0.3925,0.3925,0.3925 265 | 9NJ,0.3944,0.3944,0.3944,0.3944,0.3944,0.3944,0.3944 266 | 1OB,0.3964,0.3964,0.3964,0.3964,0.3964,0.3964,0.3964 267 | 07A,0.3984,0.3984,0.3984,0.3984,0.3984,0.3984,0.3984 268 | IP4,0.4003,0.4003,0.4003,0.4003,0.4003,0.4003,0.4003 269 | IVN,0.4023,0.4023,0.4023,0.4023,0.4023,0.4023,0.4023 270 | PAH,0.4043,0.4043,0.4043,0.4043,0.4043,0.4043,0.4043 271 | 9NG,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064 272 | O5R,0.4084,0.4084,0.4084,0.4084,0.4084,0.4084,0.4084 273 | DRA,0.4104,0.4104,0.4104,0.4104,0.4104,0.4104,0.4104 274 | ZFG,0.4125,0.4125,0.4125,0.4125,0.4125,0.4125,0.4125 275 | N8X,0.4145,0.4145,0.4145,0.4145,0.4145,0.4145,0.4145 276 | 7PC,0.4166,0.4166,0.4166,0.4166,0.4166,0.4166,0.4166 277 | ZL3,0.4187,0.4187,0.4187,0.4187,0.4187,0.4187,0.4187 278 | FI1,0.4208,0.4208,0.4208,0.4208,0.4208,0.4208,0.4208 279 | JD7,0.4229,0.4229,0.4229,0.4229,0.4229,0.4229,0.4229 280 | XAD,0.4250,0.4250,0.4250,0.4250,0.4250,0.4250,0.4250 281 | 2XR,0.4271,0.4271,0.4271,0.4271,0.4271,0.4271,0.4271 282 | S4N,0.4292,0.4292,0.4292,0.4292,0.4292,0.4292,0.4292 283 | 5HR,0.4314,0.4314,0.4314,0.4314,0.4314,0.4314,0.4314 284 | DCC,0.4335,0.4335,0.4335,0.4335,0.4335,0.4335,0.4335 285 | K3I,0.4357,0.4357,0.4357,0.4357,0.4357,0.4357,0.4357 286 | E6Y,0.4379,0.4379,0.4379,0.4379,0.4379,0.4379,0.4379 287 | DH4,0.4400,0.4400,0.4400,0.4400,0.4400,0.4400,0.4400 288 | UIS,0.4422,0.4422,0.4422,0.4422,0.4422,0.4422,0.4422 289 | 5V4,0.4444,0.4444,0.4444,0.4444,0.4444,0.4444,0.4444 290 | UIU,0.4467,0.4467,0.4467,0.4467,0.4467,0.4467,0.4467 291 | WD8,0.4489,0.4489,0.4489,0.4489,0.4489,0.4489,0.4489 292 | ELT,0.4511,0.4511,0.4511,0.4511,0.4511,0.4511,0.4511 293 | XSX,0.4534,0.4534,0.4534,0.4534,0.4534,0.4534,0.4534 294 | ZGB,0.4556,0.4556,0.4556,0.4556,0.4556,0.4556,0.4556 295 | XSC,0.4579,0.4579,0.4579,0.4579,0.4579,0.4579,0.4579 296 | D6V,0.4602,0.4602,0.4602,0.4602,0.4602,0.4602,0.4602 297 | 7L8,0.4625,0.4625,0.4625,0.4625,0.4625,0.4625,0.4625 298 | 40F,0.4648,0.4648,0.4648,0.4648,0.4648,0.4648,0.4648 299 | 3TN,0.4671,0.4671,0.4671,0.4671,0.4671,0.4671,0.4671 300 | FIV,0.4694,0.4694,0.4694,0.4694,0.4694,0.4694,0.4694 301 | PTA,0.4718,0.4718,0.4718,0.4718,0.4718,0.4718,0.4718 302 | 674,0.4741,0.4741,0.4741,0.4741,0.4741,0.4741,0.4741 303 | SCT,0.4765,0.4765,0.4765,0.4765,0.4765,0.4765,0.4765 304 | BFJ,0.4789,0.4789,0.4789,0.4789,0.4789,0.4789,0.4789 305 | MNC,0.4813,0.4813,0.4813,0.4813,0.4813,0.4813,0.4813 306 | 6K5,0.4837,0.4837,0.4837,0.4837,0.4837,0.4837,0.4837 307 | SCS,0.4861,0.4861,0.4861,0.4861,0.4861,0.4861,0.4861 308 | N2P,0.4885,0.4885,0.4885,0.4885,0.4885,0.4885,0.4885 309 | PBQ,0.4909,0.4909,0.4909,0.4909,0.4909,0.4909,0.4909 310 | DRW,0.4934,0.4934,0.4934,0.4934,0.4934,0.4934,0.4934 311 | B6P,0.4958,0.4958,0.4958,0.4958,0.4958,0.4958,0.4958 312 | 54Y,0.4983,0.4983,0.4983,0.4983,0.4983,0.4983,0.4983 313 | J7T,0.5008,0.5008,0.5008,0.5008,0.5008,0.5008,0.5008 314 | PI4,0.5033,0.5033,0.5033,0.5033,0.5033,0.5033,0.5033 315 | PBE,0.5058,0.5058,0.5058,0.5058,0.5058,0.5058,0.5058 316 | N2V,0.5083,0.5083,0.5083,0.5083,0.5083,0.5083,0.5083 317 | 2CE,0.5109,0.5109,0.5109,0.5109,0.5109,0.5109,0.5109 318 | JNS,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134 319 | SC7,0.5160,0.5160,0.5160,0.5160,0.5160,0.5160,0.5160 320 | J2W,0.5185,0.5185,0.5185,0.5185,0.5185,0.5185,0.5185 321 | 6SL,0.5211,0.5211,0.5211,0.5211,0.5211,0.5211,0.5211 322 | TYI,0.5237,0.5237,0.5237,0.5237,0.5237,0.5237,0.5237 323 | CJT,0.5263,0.5263,0.5263,0.5263,0.5263,0.5263,0.5263 324 | E7M,0.5290,0.5290,0.5290,0.5290,0.5290,0.5290,0.5290 325 | 2V6,0.5316,0.5316,0.5316,0.5316,0.5316,0.5316,0.5316 326 | SI4,0.5343,0.5343,0.5343,0.5343,0.5343,0.5343,0.5343 327 | UWE,0.5369,0.5369,0.5369,0.5369,0.5369,0.5369,0.5369 328 | SQV,0.5396,0.5396,0.5396,0.5396,0.5396,0.5396,0.5396 329 | 0TL,0.5423,0.5423,0.5423,0.5423,0.5423,0.5423,0.5423 330 | 1O8,0.5450,0.5450,0.5450,0.5450,0.5450,0.5450,0.5450 331 | MWR,0.5477,0.5477,0.5477,0.5477,0.5477,0.5477,0.5477 332 | EBU,0.5504,0.5504,0.5504,0.5504,0.5504,0.5504,0.5504 333 | XE4,0.5532,0.5532,0.5532,0.5532,0.5532,0.5532,0.5532 334 | I61,0.5560,0.5560,0.5560,0.5560,0.5560,0.5560,0.5560 335 | K9X,0.5587,0.5587,0.5587,0.5587,0.5587,0.5587,0.5587 336 | B91,0.5615,0.5615,0.5615,0.5615,0.5615,0.5615,0.5615 337 | HFT,0.5643,0.5643,0.5643,0.5643,0.5643,0.5643,0.5643 338 | YQ2,0.5671,0.5671,0.5671,0.5671,0.5671,0.5671,0.5671 339 | X7Y,0.5700,0.5700,0.5700,0.5700,0.5700,0.5700,0.5700 340 | 2CG,0.5728,0.5728,0.5728,0.5728,0.5728,0.5728,0.5728 341 | ME4,0.5757,0.5757,0.5757,0.5757,0.5757,0.5757,0.5757 342 | FW0,0.5785,0.5785,0.5785,0.5785,0.5785,0.5785,0.5785 343 | WHW,0.5814,0.5814,0.5814,0.5814,0.5814,0.5814,0.5814 344 | C3P,0.5843,0.5843,0.5843,0.5843,0.5843,0.5843,0.5843 345 | X7I,0.5872,0.5872,0.5872,0.5872,0.5872,0.5872,0.5872 346 | C56,0.5902,0.5902,0.5902,0.5902,0.5902,0.5902,0.5902 347 | TEV,0.5931,0.5931,0.5931,0.5931,0.5931,0.5931,0.5931 348 | 7JM,0.5961,0.5961,0.5961,0.5961,0.5961,0.5961,0.5961 349 | L5P,0.5990,0.5990,0.5990,0.5990,0.5990,0.5990,0.5990 350 | YSO,0.6020,0.6020,0.6020,0.6020,0.6020,0.6020,0.6020 351 | ACV,0.6050,0.6050,0.6050,0.6050,0.6050,0.6050,0.6050 352 | WW0,0.6080,0.6080,0.6080,0.6080,0.6080,0.6080,0.6080 353 | 1LH,0.6111,0.6111,0.6111,0.6111,0.6111,0.6111,0.6111 354 | J87,0.6141,0.6141,0.6141,0.6141,0.6141,0.6141,0.6141 355 | 1LB,0.6172,0.6172,0.6172,0.6172,0.6172,0.6172,0.6172 356 | 7B1,0.6203,0.6203,0.6203,0.6203,0.6203,0.6203,0.6203 357 | J6Z,0.6234,0.6234,0.6234,0.6234,0.6234,0.6234,0.6234 358 | R6J,0.6265,0.6265,0.6265,0.6265,0.6265,0.6265,0.6265 359 | XNF,0.6296,0.6296,0.6296,0.6296,0.6296,0.6296,0.6296 360 | 0RK,0.6327,0.6327,0.6327,0.6327,0.6327,0.6327,0.6327 361 | 0T9,0.6359,0.6359,0.6359,0.6359,0.6359,0.6359,0.6359 362 | BLC,0.6391,0.6391,0.6391,0.6391,0.6391,0.6391,0.6391 363 | HFG,0.6422,0.6422,0.6422,0.6422,0.6422,0.6422,0.6422 364 | ZWZ,0.6454,0.6454,0.6454,0.6454,0.6454,0.6454,0.6454 365 | QCC,0.6487,0.6487,0.6487,0.6487,0.6487,0.6487,0.6487 366 | KC1,0.6519,0.6519,0.6519,0.6519,0.6519,0.6519,0.6519 367 | NEQ,0.6552,0.6552,0.6552,0.6552,0.6552,0.6552,0.6552 368 | 05B,0.6584,0.6584,0.6584,0.6584,0.6584,0.6584,0.6584 369 | HQE,0.6617,0.6617,0.6617,0.6617,0.6617,0.6617,0.6617 370 | 84J,0.6650,0.6650,0.6650,0.6650,0.6650,0.6650,0.6650 371 | KL5,0.6683,0.6683,0.6683,0.6683,0.6683,0.6683,0.6683 372 | 4ZO,0.6717,0.6717,0.6717,0.6717,0.6717,0.6717,0.6717 373 | SJ9,0.6750,0.6750,0.6750,0.6750,0.6750,0.6750,0.6750 374 | 22B,0.6784,0.6784,0.6784,0.6784,0.6784,0.6784,0.6784 375 | ESF,0.6818,0.6818,0.6818,0.6818,0.6818,0.6818,0.6818 376 | W3D,0.6852,0.6852,0.6852,0.6852,0.6852,0.6852,0.6852 377 | R7N,0.6886,0.6886,0.6886,0.6886,0.6886,0.6886,0.6886 378 | NLV,0.6920,0.6920,0.6920,0.6920,0.6920,0.6920,0.6920 379 | YGP,0.6955,0.6955,0.6955,0.6955,0.6955,0.6955,0.6955 380 | EF0,0.6989,0.6989,0.6989,0.6989,0.6989,0.6989,0.6989 381 | J7L,0.7024,0.7024,0.7024,0.7024,0.7024,0.7024,0.7024 382 | BL1,0.7059,0.7059,0.7059,0.7059,0.7059,0.7059,0.7059 383 | EOJ,0.7094,0.7094,0.7094,0.7094,0.7094,0.7094,0.7094 384 | EF5,0.7130,0.7130,0.7130,0.7130,0.7130,0.7130,0.7130 385 | RYW,0.7165,0.7165,0.7165,0.7165,0.7165,0.7165,0.7165 386 | BL2,0.7201,0.7201,0.7201,0.7201,0.7201,0.7201,0.7201 387 | 1A2,0.7237,0.7237,0.7237,0.7237,0.7237,0.7237,0.7237 388 | VG9,0.7273,0.7273,0.7273,0.7273,0.7273,0.7273,0.7273 389 | 9PO,0.7309,0.7309,0.7309,0.7309,0.7309,0.7309,0.7309 390 | M2J,0.7346,0.7346,0.7346,0.7346,0.7346,0.7346,0.7346 391 | 81M,0.7382,0.7382,0.7382,0.7382,0.7382,0.7382,0.7382 392 | YK4,0.7419,0.7419,0.7419,0.7419,0.7419,0.7419,0.7419 393 | EXI,0.7456,0.7456,0.7456,0.7456,0.7456,0.7456,0.7456 394 | 4RQ,0.7493,0.7493,0.7493,0.7493,0.7493,0.7493,0.7493 395 | OY9,0.7531,0.7531,0.7531,0.7531,0.7531,0.7531,0.7531 396 | OSD,0.7568,0.7568,0.7568,0.7568,0.7568,0.7568,0.7568 397 | OY7,0.7606,0.7606,0.7606,0.7606,0.7606,0.7606,0.7606 398 | 91M,0.7644,0.7644,0.7644,0.7644,0.7644,0.7644,0.7644 399 | B66,0.7682,0.7682,0.7682,0.7682,0.7682,0.7682,0.7682 400 | Z54,0.7720,0.7720,0.7720,0.7720,0.7720,0.7720,0.7720 401 | 2QW,0.7759,0.7759,0.7759,0.7759,0.7759,0.7759,0.7759 402 | 3BZ,0.7798,0.7798,0.7798,0.7798,0.7798,0.7798,0.7798 403 | F04,0.7837,0.7837,0.7837,0.7837,0.7837,0.7837,0.7837 404 | 3L0,0.7876,0.7876,0.7876,0.7876,0.7876,0.7876,0.7876 405 | PCC,0.7915,0.7915,0.7915,0.7915,0.7915,0.7915,0.7915 406 | UV7,0.7954,0.7954,0.7954,0.7954,0.7954,0.7954,0.7954 407 | 5U0,0.7994,0.7994,0.7994,0.7994,0.7994,0.7994,0.7994 408 | GKI,0.8034,0.8034,0.8034,0.8034,0.8034,0.8034,0.8034 409 | ZV6,0.8074,0.8074,0.8074,0.8074,0.8074,0.8074,0.8074 410 | PBM,0.8114,0.8114,0.8114,0.8114,0.8114,0.8114,0.8114 411 | JRA,0.8155,0.8155,0.8155,0.8155,0.8155,0.8155,0.8155 412 | RD1,0.8195,0.8195,0.8195,0.8195,0.8195,0.8195,0.8195 413 | NE5,0.8236,0.8236,0.8236,0.8236,0.8236,0.8236,0.8236 414 | O4R,0.8277,0.8277,0.8277,0.8277,0.8277,0.8277,0.8277 415 | 6P7,0.8319,0.8319,0.8319,0.8319,0.8319,0.8319,0.8319 416 | DSC,0.8360,0.8360,0.8360,0.8360,0.8360,0.8360,0.8360 417 | INZ,0.8402,0.8402,0.8402,0.8402,0.8402,0.8402,0.8402 418 | 1NJ,0.8444,0.8444,0.8444,0.8444,0.8444,0.8444,0.8444 419 | N9O,0.8486,0.8486,0.8486,0.8486,0.8486,0.8486,0.8486 420 | LRC,0.8528,0.8528,0.8528,0.8528,0.8528,0.8528,0.8528 421 | GLN,0.8571,0.8571,0.8571,0.8571,0.8571,0.8571,0.8571 422 | 5MQ,0.8613,0.8613,0.8613,0.8613,0.8613,0.8613,0.8613 423 | 0C3,0.8656,0.8656,0.8656,0.8656,0.8656,0.8656,0.8656 424 | UHN,0.8700,0.8700,0.8700,0.8700,0.8700,0.8700,0.8700 425 | GLE,0.8743,0.8743,0.8743,0.8743,0.8743,0.8743,0.8743 426 | MXU,0.8787,0.8787,0.8787,0.8787,0.8787,0.8787,0.8787 427 | AKF,0.8830,0.8830,0.8830,0.8830,0.8830,0.8830,0.8830 428 | M3R,0.8874,0.8874,0.8874,0.8874,0.8874,0.8874,0.8874 429 | Y5U,0.8919,0.8919,0.8919,0.8919,0.8919,0.8919,0.8919 430 | 3MQ,0.8963,0.8963,0.8963,0.8963,0.8963,0.8963,0.8963 431 | BSK,0.9008,0.9008,0.9008,0.9008,0.9008,0.9008,0.9008 432 | D7G,0.9053,0.9053,0.9053,0.9053,0.9053,0.9053,0.9053 433 | JRZ,0.9098,0.9098,0.9098,0.9098,0.9098,0.9098,0.9098 434 | LCA,0.9143,0.9143,0.9143,0.9143,0.9143,0.9143,0.9143 435 | KG4,0.9189,0.9189,0.9189,0.9189,0.9189,0.9189,0.9189 436 | LZA,0.9235,0.9235,0.9235,0.9235,0.9235,0.9235,0.9235 437 | AUR,0.9281,0.9281,0.9281,0.9281,0.9281,0.9281,0.9281 438 | QHK,0.9327,0.9327,0.9327,0.9327,0.9327,0.9327,0.9327 439 | 3MY,0.9374,0.9374,0.9374,0.9374,0.9374,0.9374,0.9374 440 | CBY,0.9420,0.9420,0.9420,0.9420,0.9420,0.9420,0.9420 441 | R2I,0.9467,0.9467,0.9467,0.9467,0.9467,0.9467,0.9467 442 | TBD,0.9515,0.9515,0.9515,0.9515,0.9515,0.9515,0.9515 443 | Z2D,0.9562,0.9562,0.9562,0.9562,0.9562,0.9562,0.9562 444 | MPY,0.9610,0.9610,0.9610,0.9610,0.9610,0.9610,0.9610 445 | PUY,0.9658,0.9658,0.9658,0.9658,0.9658,0.9658,0.9658 446 | QN5,0.9706,0.9706,0.9706,0.9706,0.9706,0.9706,0.9706 447 | KON,0.9754,0.9754,0.9754,0.9754,0.9754,0.9754,0.9754 448 | 0UZ,0.9803,0.9803,0.9803,0.9803,0.9803,0.9803,0.9803 449 | XHO,0.9852,0.9852,0.9852,0.9852,0.9852,0.9852,0.9852 450 | Z20,0.9901,0.9901,0.9901,0.9901,0.9901,0.9901,0.9901 451 | XHZ,0.9950,0.9950,0.9950,0.9950,0.9950,0.9950,0.9950 452 | FSI,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 453 | 7F7,1.0050,1.0050,1.0050,1.0050,1.0050,1.0050,1.0050 454 | W5X,1.0100,1.0100,1.0100,1.0100,1.0100,1.0100,1.0100 455 | YNG,1.0150,1.0150,1.0150,1.0150,1.0150,1.0150,1.0150 456 | ZHK,1.0201,1.0201,1.0201,1.0201,1.0201,1.0201,1.0201 457 | YNT,1.0252,1.0252,1.0252,1.0252,1.0252,1.0252,1.0252 458 | OUF,1.0303,1.0303,1.0303,1.0303,1.0303,1.0303,1.0303 459 | 146,1.0354,1.0354,1.0354,1.0354,1.0354,1.0354,1.0354 460 | S0J,1.0406,1.0406,1.0406,1.0406,1.0406,1.0406,1.0406 461 | DGY,1.0458,1.0458,1.0458,1.0458,1.0458,1.0458,1.0458 462 | JDN,1.0510,1.0510,1.0510,1.0510,1.0510,1.0510,1.0510 463 | 3YN,1.0563,1.0563,1.0563,1.0563,1.0563,1.0563,1.0563 464 | 6NS,1.0615,1.0615,1.0615,1.0615,1.0615,1.0615,1.0615 465 | BA2,1.0668,1.0668,1.0668,1.0668,1.0668,1.0668,1.0668 466 | FKQ,1.0721,1.0721,1.0721,1.0721,1.0721,1.0721,1.0721 467 | QLV,1.0775,1.0775,1.0775,1.0775,1.0775,1.0775,1.0775 468 | 4ZR,1.0829,1.0829,1.0829,1.0829,1.0829,1.0829,1.0829 469 | 7O9,1.0883,1.0883,1.0883,1.0883,1.0883,1.0883,1.0883 470 | EKR,1.0937,1.0937,1.0937,1.0937,1.0937,1.0937,1.0937 471 | 1AC,1.0991,1.0991,1.0991,1.0991,1.0991,1.0991,1.0991 472 | QMY,1.1046,1.1046,1.1046,1.1046,1.1046,1.1046,1.1046 473 | 23Q,1.1101,1.1101,1.1101,1.1101,1.1101,1.1101,1.1101 474 | IE8,1.1157,1.1157,1.1157,1.1157,1.1157,1.1157,1.1157 475 | I85,1.1212,1.1212,1.1212,1.1212,1.1212,1.1212,1.1212 476 | 2DQ,1.1268,1.1268,1.1268,1.1268,1.1268,1.1268,1.1268 477 | 096,1.1324,1.1324,1.1324,1.1324,1.1324,1.1324,1.1324 478 | 1BV,1.1381,1.1381,1.1381,1.1381,1.1381,1.1381,1.1381 479 | LIQ,1.1438,1.1438,1.1438,1.1438,1.1438,1.1438,1.1438 480 | 63W,1.1495,1.1495,1.1495,1.1495,1.1495,1.1495,1.1495 481 | QB0,1.1552,1.1552,1.1552,1.1552,1.1552,1.1552,1.1552 482 | NBP,1.1610,1.1610,1.1610,1.1610,1.1610,1.1610,1.1610 483 | 4PO,1.1668,1.1668,1.1668,1.1668,1.1668,1.1668,1.1668 484 | AOU,1.1726,1.1726,1.1726,1.1726,1.1726,1.1726,1.1726 485 | TP5,1.1784,1.1784,1.1784,1.1784,1.1784,1.1784,1.1784 486 | GT0,1.1843,1.1843,1.1843,1.1843,1.1843,1.1843,1.1843 487 | TP1,1.1902,1.1902,1.1902,1.1902,1.1902,1.1902,1.1902 488 | 5JO,1.1961,1.1961,1.1961,1.1961,1.1961,1.1961,1.1961 489 | TPC,1.2021,1.2021,1.2021,1.2021,1.2021,1.2021,1.2021 490 | OLT,1.2081,1.2081,1.2081,1.2081,1.2081,1.2081,1.2081 491 | 93R,1.2141,1.2141,1.2141,1.2141,1.2141,1.2141,1.2141 492 | T9R,1.2202,1.2202,1.2202,1.2202,1.2202,1.2202,1.2202 493 | HRC,1.2263,1.2263,1.2263,1.2263,1.2263,1.2263,1.2263 494 | 9KU,1.2324,1.2324,1.2324,1.2324,1.2324,1.2324,1.2324 495 | CZ8,1.2385,1.2385,1.2385,1.2385,1.2385,1.2385,1.2385 496 | Q8Z,1.2447,1.2447,1.2447,1.2447,1.2447,1.2447,1.2447 497 | JR0,1.2509,1.2509,1.2509,1.2509,1.2509,1.2509,1.2509 498 | QTH,1.2572,1.2572,1.2572,1.2572,1.2572,1.2572,1.2572 499 | 8CF,1.2634,1.2634,1.2634,1.2634,1.2634,1.2634,1.2634 500 | J7G,1.2697,1.2697,1.2697,1.2697,1.2697,1.2697,1.2697 501 | RTA,1.2761,1.2761,1.2761,1.2761,1.2761,1.2761,1.2761 502 | GG4,1.2824,1.2824,1.2824,1.2824,1.2824,1.2824,1.2824 503 | UGE,1.2888,1.2888,1.2888,1.2888,1.2888,1.2888,1.2888 504 | DFY,1.2953,1.2953,1.2953,1.2953,1.2953,1.2953,1.2953 505 | EN6,1.3017,1.3017,1.3017,1.3017,1.3017,1.3017,1.3017 506 | AUD,1.3082,1.3082,1.3082,1.3082,1.3082,1.3082,1.3082 507 | 8GM,1.3147,1.3147,1.3147,1.3147,1.3147,1.3147,1.3147 508 | QFZ,1.3213,1.3213,1.3213,1.3213,1.3213,1.3213,1.3213 509 | OXM,1.3279,1.3279,1.3279,1.3279,1.3279,1.3279,1.3279 510 | OD0,1.3345,1.3345,1.3345,1.3345,1.3345,1.3345,1.3345 511 | V4E,1.3412,1.3412,1.3412,1.3412,1.3412,1.3412,1.3412 512 | JZF,1.3478,1.3478,1.3478,1.3478,1.3478,1.3478,1.3478 513 | 4NY,1.3546,1.3546,1.3546,1.3546,1.3546,1.3546,1.3546 514 | 2NG,1.3613,1.3613,1.3613,1.3613,1.3613,1.3613,1.3613 515 | 2TE,1.3681,1.3681,1.3681,1.3681,1.3681,1.3681,1.3681 516 | 565,1.3749,1.3749,1.3749,1.3749,1.3749,1.3749,1.3749 517 | GV1,1.3818,1.3818,1.3818,1.3818,1.3818,1.3818,1.3818 518 | KJL,1.3887,1.3887,1.3887,1.3887,1.3887,1.3887,1.3887 519 | ISP,1.3956,1.3956,1.3956,1.3956,1.3956,1.3956,1.3956 520 | VKJ,1.4026,1.4026,1.4026,1.4026,1.4026,1.4026,1.4026 521 | ATX,1.4096,1.4096,1.4096,1.4096,1.4096,1.4096,1.4096 522 | 2NY,1.4166,1.4166,1.4166,1.4166,1.4166,1.4166,1.4166 523 | Z3S,1.4237,1.4237,1.4237,1.4237,1.4237,1.4237,1.4237 524 | KRN,1.4308,1.4308,1.4308,1.4308,1.4308,1.4308,1.4308 525 | NBH,1.4379,1.4379,1.4379,1.4379,1.4379,1.4379,1.4379 526 | D9I,1.4451,1.4451,1.4451,1.4451,1.4451,1.4451,1.4451 527 | SEO,1.4523,1.4523,1.4523,1.4523,1.4523,1.4523,1.4523 528 | VEN,1.4595,1.4595,1.4595,1.4595,1.4595,1.4595,1.4595 529 | 3VT,1.4668,1.4668,1.4668,1.4668,1.4668,1.4668,1.4668 530 | TE2,1.4741,1.4741,1.4741,1.4741,1.4741,1.4741,1.4741 531 | CLM,1.4815,1.4815,1.4815,1.4815,1.4815,1.4815,1.4815 532 | 6G1,1.4889,1.4889,1.4889,1.4889,1.4889,1.4889,1.4889 533 | PDX,1.4963,1.4963,1.4963,1.4963,1.4963,1.4963,1.4963 534 | FZE,1.5038,1.5038,1.5038,1.5038,1.5038,1.5038,1.5038 535 | NBL,1.5113,1.5113,1.5113,1.5113,1.5113,1.5113,1.5113 536 | 3DK,1.5188,1.5188,1.5188,1.5188,1.5188,1.5188,1.5188 537 | 4NJ,1.5264,1.5264,1.5264,1.5264,1.5264,1.5264,1.5264 538 | 3G8,1.5340,1.5340,1.5340,1.5340,1.5340,1.5340,1.5340 539 | QZ9,1.5416,1.5416,1.5416,1.5416,1.5416,1.5416,1.5416 540 | RLN,1.5493,1.5493,1.5493,1.5493,1.5493,1.5493,1.5493 541 | 1YU,1.5570,1.5570,1.5570,1.5570,1.5570,1.5570,1.5570 542 | RE1,1.5648,1.5648,1.5648,1.5648,1.5648,1.5648,1.5648 543 | 62U,1.5726,1.5726,1.5726,1.5726,1.5726,1.5726,1.5726 544 | JJQ,1.5805,1.5805,1.5805,1.5805,1.5805,1.5805,1.5805 545 | U89,1.5883,1.5883,1.5883,1.5883,1.5883,1.5883,1.5883 546 | 9LF,1.5963,1.5963,1.5963,1.5963,1.5963,1.5963,1.5963 547 | M13,1.6042,1.6042,1.6042,1.6042,1.6042,1.6042,1.6042 548 | I2I,1.6122,1.6122,1.6122,1.6122,1.6122,1.6122,1.6122 549 | J49,1.6203,1.6203,1.6203,1.6203,1.6203,1.6203,1.6203 550 | OMQ,1.6283,1.6283,1.6283,1.6283,1.6283,1.6283,1.6283 551 | VZA,1.6365,1.6365,1.6365,1.6365,1.6365,1.6365,1.6365 552 | 7PL,1.6446,1.6446,1.6446,1.6446,1.6446,1.6446,1.6446 553 | J42,1.6528,1.6528,1.6528,1.6528,1.6528,1.6528,1.6528 554 | Z8H,1.6611,1.6611,1.6611,1.6611,1.6611,1.6611,1.6611 555 | 1YP,1.6694,1.6694,1.6694,1.6694,1.6694,1.6694,1.6694 556 | 8BE,1.6777,1.6777,1.6777,1.6777,1.6777,1.6777,1.6777 557 | A8B,1.6861,1.6861,1.6861,1.6861,1.6861,1.6861,1.6861 558 | QNM,1.6945,1.6945,1.6945,1.6945,1.6945,1.6945,1.6945 559 | 20G,1.7029,1.7029,1.7029,1.7029,1.7029,1.7029,1.7029 560 | 9LH,1.7114,1.7114,1.7114,1.7114,1.7114,1.7114,1.7114 561 | T5Y,1.7199,1.7199,1.7199,1.7199,1.7199,1.7199,1.7199 562 | NE1,1.7285,1.7285,1.7285,1.7285,1.7285,1.7285,1.7285 563 | IWK,1.7371,1.7371,1.7371,1.7371,1.7371,1.7371,1.7371 564 | R2O,1.7458,1.7458,1.7458,1.7458,1.7458,1.7458,1.7458 565 | GOY,1.7545,1.7545,1.7545,1.7545,1.7545,1.7545,1.7545 566 | GQK,1.7633,1.7633,1.7633,1.7633,1.7633,1.7633,1.7633 567 | DHT,1.7721,1.7721,1.7721,1.7721,1.7721,1.7721,1.7721 568 | FHH,1.7809,1.7809,1.7809,1.7809,1.7809,1.7809,1.7809 569 | JY8,1.7898,1.7898,1.7898,1.7898,1.7898,1.7898,1.7898 570 | EIW,1.7987,1.7987,1.7987,1.7987,1.7987,1.7987,1.7987 571 | BP6,1.8077,1.8077,1.8077,1.8077,1.8077,1.8077,1.8077 572 | BPK,1.8167,1.8167,1.8167,1.8167,1.8167,1.8167,1.8167 573 | H6R,1.8258,1.8258,1.8258,1.8258,1.8258,1.8258,1.8258 574 | CEO,1.8349,1.8349,1.8349,1.8349,1.8349,1.8349,1.8349 575 | HSF,1.8440,1.8440,1.8440,1.8440,1.8440,1.8440,1.8440 576 | 552,1.8532,1.8532,1.8532,1.8532,1.8532,1.8532,1.8532 577 | RIB,1.8625,1.8625,1.8625,1.8625,1.8625,1.8625,1.8625 578 | T6N,1.8717,1.8717,1.8717,1.8717,1.8717,1.8717,1.8717 579 | LDV,1.8811,1.8811,1.8811,1.8811,1.8811,1.8811,1.8811 580 | B4B,1.8905,1.8905,1.8905,1.8905,1.8905,1.8905,1.8905 581 | TNZ,1.8999,1.8999,1.8999,1.8999,1.8999,1.8999,1.8999 582 | 065,1.9094,1.9094,1.9094,1.9094,1.9094,1.9094,1.9094 583 | KNP,1.9189,1.9189,1.9189,1.9189,1.9189,1.9189,1.9189 584 | CVD,1.9285,1.9285,1.9285,1.9285,1.9285,1.9285,1.9285 585 | SYJ,1.9381,1.9381,1.9381,1.9381,1.9381,1.9381,1.9381 586 | QCH,1.9477,1.9477,1.9477,1.9477,1.9477,1.9477,1.9477 587 | 920,1.9575,1.9575,1.9575,1.9575,1.9575,1.9575,1.9575 588 | 1PV,1.9672,1.9672,1.9672,1.9672,1.9672,1.9672,1.9672 589 | VZZ,1.9770,1.9770,1.9770,1.9770,1.9770,1.9770,1.9770 590 | CAF,1.9869,1.9869,1.9869,1.9869,1.9869,1.9869,1.9869 591 | RGW,1.9968,1.9968,1.9968,1.9968,1.9968,1.9968,1.9968 592 | C0J,2.0068,2.0068,2.0068,2.0068,2.0068,2.0068,2.0068 593 | 5ED,2.0168,2.0168,2.0168,2.0168,2.0168,2.0168,2.0168 594 | 6W8,2.0268,2.0268,2.0268,2.0268,2.0268,2.0268,2.0268 595 | Q2M,2.0369,2.0369,2.0369,2.0369,2.0369,2.0369,2.0369 596 | F5Q,2.0471,2.0471,2.0471,2.0471,2.0471,2.0471,2.0471 597 | H88,2.0573,2.0573,2.0573,2.0573,2.0573,2.0573,2.0573 598 | 953,2.0676,2.0676,2.0676,2.0676,2.0676,2.0676,2.0676 599 | 9VW,2.0779,2.0779,2.0779,2.0779,2.0779,2.0779,2.0779 600 | NCJ,2.0882,2.0882,2.0882,2.0882,2.0882,2.0882,2.0882 601 | G7U,2.0987,2.0987,2.0987,2.0987,2.0987,2.0987,2.0987 602 | FX3,2.1091,2.1091,2.1091,2.1091,2.1091,2.1091,2.1091 603 | 95A,2.1196,2.1196,2.1196,2.1196,2.1196,2.1196,2.1196 604 | 2VV,2.1302,2.1302,2.1302,2.1302,2.1302,2.1302,2.1302 605 | V55,2.1408,2.1408,2.1408,2.1408,2.1408,2.1408,2.1408 606 | 61N,2.1515,2.1515,2.1515,2.1515,2.1515,2.1515,2.1515 607 | YKX,2.1623,2.1623,2.1623,2.1623,2.1623,2.1623,2.1623 608 | AB8,2.1730,2.1730,2.1730,2.1730,2.1730,2.1730,2.1730 609 | 8X5,2.1839,2.1839,2.1839,2.1839,2.1839,2.1839,2.1839 610 | PJX,2.1948,2.1948,2.1948,2.1948,2.1948,2.1948,2.1948 611 | 4QK,2.2057,2.2057,2.2057,2.2057,2.2057,2.2057,2.2057 612 | EJM,2.2167,2.2167,2.2167,2.2167,2.2167,2.2167,2.2167 613 | 5E3,2.2278,2.2278,2.2278,2.2278,2.2278,2.2278,2.2278 614 | FXY,2.2389,2.2389,2.2389,2.2389,2.2389,2.2389,2.2389 615 | 2G1,2.2500,2.2500,2.2500,2.2500,2.2500,2.2500,2.2500 616 | 083,2.2613,2.2613,2.2613,2.2613,2.2613,2.2613,2.2613 617 | 6VS,2.2725,2.2725,2.2725,2.2725,2.2725,2.2725,2.2725 618 | YOD,2.2839,2.2839,2.2839,2.2839,2.2839,2.2839,2.2839 619 | 3GV,2.2953,2.2953,2.2953,2.2953,2.2953,2.2953,2.2953 620 | 1QI,2.3067,2.3067,2.3067,2.3067,2.3067,2.3067,2.3067 621 | FSU,2.3182,2.3182,2.3182,2.3182,2.3182,2.3182,2.3182 622 | HXM,2.3298,2.3298,2.3298,2.3298,2.3298,2.3298,2.3298 623 | UF3,2.3414,2.3414,2.3414,2.3414,2.3414,2.3414,2.3414 624 | QR0,2.3531,2.3531,2.3531,2.3531,2.3531,2.3531,2.3531 625 | TX6,2.3648,2.3648,2.3648,2.3648,2.3648,2.3648,2.3648 626 | FPW,2.3766,2.3766,2.3766,2.3766,2.3766,2.3766,2.3766 627 | 637,2.3885,2.3885,2.3885,2.3885,2.3885,2.3885,2.3885 628 | FRM,2.4004,2.4004,2.4004,2.4004,2.4004,2.4004,2.4004 629 | ZTZ,2.4124,2.4124,2.4124,2.4124,2.4124,2.4124,2.4124 630 | 7A9,2.4244,2.4244,2.4244,2.4244,2.4244,2.4244,2.4244 631 | GVU,2.4365,2.4365,2.4365,2.4365,2.4365,2.4365,2.4365 632 | ON2,2.4486,2.4486,2.4486,2.4486,2.4486,2.4486,2.4486 633 | UFA,2.4608,2.4608,2.4608,2.4608,2.4608,2.4608,2.4608 634 | W4I,2.4731,2.4731,2.4731,2.4731,2.4731,2.4731,2.4731 635 | PE7,2.4855,2.4855,2.4855,2.4855,2.4855,2.4855,2.4855 636 | 5BN,2.4979,2.4979,2.4979,2.4979,2.4979,2.4979,2.4979 637 | 5SP,2.5103,2.5103,2.5103,2.5103,2.5103,2.5103,2.5103 638 | O0I,2.5228,2.5228,2.5228,2.5228,2.5228,2.5228,2.5228 639 | 0E9,2.5354,2.5354,2.5354,2.5354,2.5354,2.5354,2.5354 640 | 7FM,2.5481,2.5481,2.5481,2.5481,2.5481,2.5481,2.5481 641 | MKW,2.5608,2.5608,2.5608,2.5608,2.5608,2.5608,2.5608 642 | AX2,2.5735,2.5735,2.5735,2.5735,2.5735,2.5735,2.5735 643 | XV8,2.5864,2.5864,2.5864,2.5864,2.5864,2.5864,2.5864 644 | ZG0,2.5993,2.5993,2.5993,2.5993,2.5993,2.5993,2.5993 645 | IR3,2.6122,2.6122,2.6122,2.6122,2.6122,2.6122,2.6122 646 | RJE,2.6253,2.6253,2.6253,2.6253,2.6253,2.6253,2.6253 647 | UTA,2.6384,2.6384,2.6384,2.6384,2.6384,2.6384,2.6384 648 | VU8,2.6515,2.6515,2.6515,2.6515,2.6515,2.6515,2.6515 649 | I8J,2.6647,2.6647,2.6647,2.6647,2.6647,2.6647,2.6647 650 | W8K,2.6780,2.6780,2.6780,2.6780,2.6780,2.6780,2.6780 651 | XVN,2.6914,2.6914,2.6914,2.6914,2.6914,2.6914,2.6914 652 | 9BF,2.7048,2.7048,2.7048,2.7048,2.7048,2.7048,2.7048 653 | Z0Z,2.7183,2.7183,2.7183,2.7183,2.7183,2.7183,2.7183 654 | 9JF,2.7319,2.7319,2.7319,2.7319,2.7319,2.7319,2.7319 655 | C1Z,2.7455,2.7455,2.7455,2.7455,2.7455,2.7455,2.7455 656 | 6DI,2.7592,2.7592,2.7592,2.7592,2.7592,2.7592,2.7592 657 | C1V,2.7729,2.7729,2.7729,2.7729,2.7729,2.7729,2.7729 658 | 6DO,2.7868,2.7868,2.7868,2.7868,2.7868,2.7868,2.7868 659 | 6DN,2.8007,2.8007,2.8007,2.8007,2.8007,2.8007,2.8007 660 | EPI,2.8146,2.8146,2.8146,2.8146,2.8146,2.8146,2.8146 661 | BOD,2.8287,2.8287,2.8287,2.8287,2.8287,2.8287,2.8287 662 | YV2,2.8428,2.8428,2.8428,2.8428,2.8428,2.8428,2.8428 663 | YV3,2.8570,2.8570,2.8570,2.8570,2.8570,2.8570,2.8570 664 | R8A,2.8712,2.8712,2.8712,2.8712,2.8712,2.8712,2.8712 665 | VVQ,2.8855,2.8855,2.8855,2.8855,2.8855,2.8855,2.8855 666 | MRA,2.8999,2.8999,2.8999,2.8999,2.8999,2.8999,2.8999 667 | SXQ,2.9144,2.9144,2.9144,2.9144,2.9144,2.9144,2.9144 668 | NKE,2.9289,2.9289,2.9289,2.9289,2.9289,2.9289,2.9289 669 | J5B,2.9435,2.9435,2.9435,2.9435,2.9435,2.9435,2.9435 670 | 0WW,2.9582,2.9582,2.9582,2.9582,2.9582,2.9582,2.9582 671 | GWS,2.9730,2.9730,2.9730,2.9730,2.9730,2.9730,2.9730 672 | VV6,2.9878,2.9878,2.9878,2.9878,2.9878,2.9878,2.9878 673 | SX9,3.0027,3.0027,3.0027,3.0027,3.0027,3.0027,3.0027 674 | SVU,3.0177,3.0177,3.0177,3.0177,3.0177,3.0177,3.0177 675 | 21W,3.0327,3.0327,3.0327,3.0327,3.0327,3.0327,3.0327 676 | NKL,3.0479,3.0479,3.0479,3.0479,3.0479,3.0479,3.0479 677 | S5O,3.0631,3.0631,3.0631,3.0631,3.0631,3.0631,3.0631 678 | KGA,3.0783,3.0783,3.0783,3.0783,3.0783,3.0783,3.0783 679 | 1GU,3.0937,3.0937,3.0937,3.0937,3.0937,3.0937,3.0937 680 | Y99,3.1091,3.1091,3.1091,3.1091,3.1091,3.1091,3.1091 681 | KGF,3.1246,3.1246,3.1246,3.1246,3.1246,3.1246,3.1246 682 | 1GD,3.1402,3.1402,3.1402,3.1402,3.1402,3.1402,3.1402 683 | PEI,3.1559,3.1559,3.1559,3.1559,3.1559,3.1559,3.1559 684 | VDR,3.1716,3.1716,3.1716,3.1716,3.1716,3.1716,3.1716 685 | I8S,3.1874,3.1874,3.1874,3.1874,3.1874,3.1874,3.1874 686 | FLR,3.2033,3.2033,3.2033,3.2033,3.2033,3.2033,3.2033 687 | LKB,3.2193,3.2193,3.2193,3.2193,3.2193,3.2193,3.2193 688 | R6N,3.2354,3.2354,3.2354,3.2354,3.2354,3.2354,3.2354 689 | R24,3.2515,3.2515,3.2515,3.2515,3.2515,3.2515,3.2515 690 | 84O,3.2677,3.2677,3.2677,3.2677,3.2677,3.2677,3.2677 691 | 84M,3.2840,3.2840,3.2840,3.2840,3.2840,3.2840,3.2840 692 | KC9,3.3004,3.3004,3.3004,3.3004,3.3004,3.3004,3.3004 693 | PAP,3.3168,3.3168,3.3168,3.3168,3.3168,3.3168,3.3168 694 | ZQQ,3.3334,3.3334,3.3334,3.3334,3.3334,3.3334,3.3334 695 | W3O,3.3500,3.3500,3.3500,3.3500,3.3500,3.3500,3.3500 696 | WVH,3.3667,3.3667,3.3667,3.3667,3.3667,3.3667,3.3667 697 | LA4,3.3835,3.3835,3.3835,3.3835,3.3835,3.3835,3.3835 698 | WLF,3.4004,3.4004,3.4004,3.4004,3.4004,3.4004,3.4004 699 | 71F,3.4174,3.4174,3.4174,3.4174,3.4174,3.4174,3.4174 700 | WV8,3.4344,3.4344,3.4344,3.4344,3.4344,3.4344,3.4344 701 | 1UD,3.4515,3.4515,3.4515,3.4515,3.4515,3.4515,3.4515 702 | 1UF,3.4687,3.4687,3.4687,3.4687,3.4687,3.4687,3.4687 703 | W38,3.4860,3.4860,3.4860,3.4860,3.4860,3.4860,3.4860 704 | 8T8,3.5034,3.5034,3.5034,3.5034,3.5034,3.5034,3.5034 705 | TRU,3.5209,3.5209,3.5209,3.5209,3.5209,3.5209,3.5209 706 | FL8,3.5385,3.5385,3.5385,3.5385,3.5385,3.5385,3.5385 707 | KCC,3.5561,3.5561,3.5561,3.5561,3.5561,3.5561,3.5561 708 | 840,3.5738,3.5738,3.5738,3.5738,3.5738,3.5738,3.5738 709 | YJ7,3.5917,3.5917,3.5917,3.5917,3.5917,3.5917,3.5917 710 | 8WA,3.6096,3.6096,3.6096,3.6096,3.6096,3.6096,3.6096 711 | P4J,3.6276,3.6276,3.6276,3.6276,3.6276,3.6276,3.6276 712 | 9U5,3.6457,3.6457,3.6457,3.6457,3.6457,3.6457,3.6457 713 | OZ6,3.6639,3.6639,3.6639,3.6639,3.6639,3.6639,3.6639 714 | EHV,3.6821,3.6821,3.6821,3.6821,3.6821,3.6821,3.6821 715 | 8KB,3.7005,3.7005,3.7005,3.7005,3.7005,3.7005,3.7005 716 | 8KC,3.7190,3.7190,3.7190,3.7190,3.7190,3.7190,3.7190 717 | 8KY,3.7375,3.7375,3.7375,3.7375,3.7375,3.7375,3.7375 718 | 756,3.7561,3.7561,3.7561,3.7561,3.7561,3.7561,3.7561 719 | EHW,3.7749,3.7749,3.7749,3.7749,3.7749,3.7749,3.7749 720 | SS1,3.7937,3.7937,3.7937,3.7937,3.7937,3.7937,3.7937 721 | RDY,3.8126,3.8126,3.8126,3.8126,3.8126,3.8126,3.8126 722 | ELZ,3.8316,3.8316,3.8316,3.8316,3.8316,3.8316,3.8316 723 | RDA,3.8508,3.8508,3.8508,3.8508,3.8508,3.8508,3.8508 724 | RDC,3.8700,3.8700,3.8700,3.8700,3.8700,3.8700,3.8700 725 | F74,3.8893,3.8893,3.8893,3.8893,3.8893,3.8893,3.8893 726 | WRC,3.9087,3.9087,3.9087,3.9087,3.9087,3.9087,3.9087 727 | 5N6,3.9282,3.9282,3.9282,3.9282,3.9282,3.9282,3.9282 728 | FBU,3.9477,3.9477,3.9477,3.9477,3.9477,3.9477,3.9477 729 | LEH,3.9674,3.9674,3.9674,3.9674,3.9674,3.9674,3.9674 730 | 9UK,3.9872,3.9872,3.9872,3.9872,3.9872,3.9872,3.9872 731 | P45,4.0071,4.0071,4.0071,4.0071,4.0071,4.0071,4.0071 732 | 607,4.0271,4.0271,4.0271,4.0271,4.0271,4.0271,4.0271 733 | IQX,4.0472,4.0472,4.0472,4.0472,4.0472,4.0472,4.0472 734 | 603,4.0674,4.0674,4.0674,4.0674,4.0674,4.0674,4.0674 735 | AZA,4.0877,4.0877,4.0877,4.0877,4.0877,4.0877,4.0877 736 | U6K,4.1080,4.1080,4.1080,4.1080,4.1080,4.1080,4.1080 737 | WRW,4.1285,4.1285,4.1285,4.1285,4.1285,4.1285,4.1285 738 | G2R,4.1491,4.1491,4.1491,4.1491,4.1491,4.1491,4.1491 739 | 9IM,4.1698,4.1698,4.1698,4.1698,4.1698,4.1698,4.1698 740 | 6EY,4.1906,4.1906,4.1906,4.1906,4.1906,4.1906,4.1906 741 | 3HD,4.2115,4.2115,4.2115,4.2115,4.2115,4.2115,4.2115 742 | C0T,4.2325,4.2325,4.2325,4.2325,4.2325,4.2325,4.2325 743 | 3HN,4.2536,4.2536,4.2536,4.2536,4.2536,4.2536,4.2536 744 | 6E7,4.2748,4.2748,4.2748,4.2748,4.2748,4.2748,4.2748 745 | LAI,4.2962,4.2962,4.2962,4.2962,4.2962,4.2962,4.2962 746 | 3FV,4.3176,4.3176,4.3176,4.3176,4.3176,4.3176,4.3176 747 | Z17,4.3391,4.3391,4.3391,4.3391,4.3391,4.3391,4.3391 748 | ICG,4.3608,4.3608,4.3608,4.3608,4.3608,4.3608,4.3608 749 | 9EV,4.3825,4.3825,4.3825,4.3825,4.3825,4.3825,4.3825 750 | ZCF,4.4044,4.4044,4.4044,4.4044,4.4044,4.4044,4.4044 751 | X0P,4.4263,4.4263,4.4263,4.4263,4.4263,4.4263,4.4263 752 | 7DD,4.4484,4.4484,4.4484,4.4484,4.4484,4.4484,4.4484 753 | XW4,4.4706,4.4706,4.4706,4.4706,4.4706,4.4706,4.4706 754 | X5S,4.4929,4.4929,4.4929,4.4929,4.4929,4.4929,4.4929 755 | F9P,4.5153,4.5153,4.5153,4.5153,4.5153,4.5153,4.5153 756 | ZCJ,4.5378,4.5378,4.5378,4.5378,4.5378,4.5378,4.5378 757 | WL0,4.5605,4.5605,4.5605,4.5605,4.5605,4.5605,4.5605 758 | DXF,4.5832,4.5832,4.5832,4.5832,4.5832,4.5832,4.5832 759 | WL1,4.6061,4.6061,4.6061,4.6061,4.6061,4.6061,4.6061 760 | DXD,4.6290,4.6290,4.6290,4.6290,4.6290,4.6290,4.6290 761 | J2E,4.6521,4.6521,4.6521,4.6521,4.6521,4.6521,4.6521 762 | J2C,4.6753,4.6753,4.6753,4.6753,4.6753,4.6753,4.6753 763 | O58,4.6987,4.6987,4.6987,4.6987,4.6987,4.6987,4.6987 764 | 3ZY,4.7221,4.7221,4.7221,4.7221,4.7221,4.7221,4.7221 765 | J2R,4.7456,4.7456,4.7456,4.7456,4.7456,4.7456,4.7456 766 | T79,4.7693,4.7693,4.7693,4.7693,4.7693,4.7693,4.7693 767 | 7CR,4.7931,4.7931,4.7931,4.7931,4.7931,4.7931,4.7931 768 | S4B,4.8170,4.8170,4.8170,4.8170,4.8170,4.8170,4.8170 769 | OVI,4.8410,4.8410,4.8410,4.8410,4.8410,4.8410,4.8410 770 | 1FL,4.8652,4.8652,4.8652,4.8652,4.8652,4.8652,4.8652 771 | 0F3,4.8894,4.8894,4.8894,4.8894,4.8894,4.8894,4.8894 772 | TAH,4.9138,4.9138,4.9138,4.9138,4.9138,4.9138,4.9138 773 | 0FB,4.9383,4.9383,4.9383,4.9383,4.9383,4.9383,4.9383 774 | 7C5,4.9630,4.9630,4.9630,4.9630,4.9630,4.9630,4.9630 775 | GIQ,4.9877,4.9877,4.9877,4.9877,4.9877,4.9877,4.9877 776 | MJC,5.0126,5.0126,5.0126,5.0126,5.0126,5.0126,5.0126 777 | MJL,5.0376,5.0376,5.0376,5.0376,5.0376,5.0376,5.0376 778 | GIZ,5.0627,5.0627,5.0627,5.0627,5.0627,5.0627,5.0627 779 | 1F2,5.0880,5.0880,5.0880,5.0880,5.0880,5.0880,5.0880 780 | JIV,5.1133,5.1133,5.1133,5.1133,5.1133,5.1133,5.1133 781 | DJC,5.1389,5.1389,5.1389,5.1389,5.1389,5.1389,5.1389 782 | JIQ,5.1645,5.1645,5.1645,5.1645,5.1645,5.1645,5.1645 783 | 64G,5.1902,5.1902,5.1902,5.1902,5.1902,5.1902,5.1902 784 | YU0,5.2161,5.2161,5.2161,5.2161,5.2161,5.2161,5.2161 785 | NPB,5.2421,5.2421,5.2421,5.2421,5.2421,5.2421,5.2421 786 | XYA,5.2683,5.2683,5.2683,5.2683,5.2683,5.2683,5.2683 787 | 64P,5.2946,5.2946,5.2946,5.2946,5.2946,5.2946,5.2946 788 | XYG,5.3210,5.3210,5.3210,5.3210,5.3210,5.3210,5.3210 789 | 3Z7,5.3475,5.3475,5.3475,5.3475,5.3475,5.3475,5.3475 790 | VW6,5.3742,5.3742,5.3742,5.3742,5.3742,5.3742,5.3742 791 | 90Y,5.4010,5.4010,5.4010,5.4010,5.4010,5.4010,5.4010 792 | 1TF,5.4279,5.4279,5.4279,5.4279,5.4279,5.4279,5.4279 793 | TST,5.4550,5.4550,5.4550,5.4550,5.4550,5.4550,5.4550 794 | HC9,5.4822,5.4822,5.4822,5.4822,5.4822,5.4822,5.4822 795 | 6FA,5.5095,5.5095,5.5095,5.5095,5.5095,5.5095,5.5095 796 | IUN,5.5370,5.5370,5.5370,5.5370,5.5370,5.5370,5.5370 797 | KPW,5.5646,5.5646,5.5646,5.5646,5.5646,5.5646,5.5646 798 | HM2,5.5924,5.5924,5.5924,5.5924,5.5924,5.5924,5.5924 799 | HU3,5.6203,5.6203,5.6203,5.6203,5.6203,5.6203,5.6203 800 | U2M,5.6483,5.6483,5.6483,5.6483,5.6483,5.6483,5.6483 801 | LZZ,5.6765,5.6765,5.6765,5.6765,5.6765,5.6765,5.6765 802 | GU3,5.7048,5.7048,5.7048,5.7048,5.7048,5.7048,5.7048 803 | YTM,5.7333,5.7333,5.7333,5.7333,5.7333,5.7333,5.7333 804 | 44L,5.7618,5.7618,5.7618,5.7618,5.7618,5.7618,5.7618 805 | 8ZW,5.7906,5.7906,5.7906,5.7906,5.7906,5.7906,5.7906 806 | IUW,5.8195,5.8195,5.8195,5.8195,5.8195,5.8195,5.8195 807 | EKQ,5.8485,5.8485,5.8485,5.8485,5.8485,5.8485,5.8485 808 | N0N,5.8777,5.8777,5.8777,5.8777,5.8777,5.8777,5.8777 809 | O8T,5.9070,5.9070,5.9070,5.9070,5.9070,5.9070,5.9070 810 | 440,5.9364,5.9364,5.9364,5.9364,5.9364,5.9364,5.9364 811 | YI7,5.9660,5.9660,5.9660,5.9660,5.9660,5.9660,5.9660 812 | U24,5.9958,5.9958,5.9958,5.9958,5.9958,5.9958,5.9958 813 | FCH,6.0257,6.0257,6.0257,6.0257,6.0257,6.0257,6.0257 814 | 9TE,6.0558,6.0558,6.0558,6.0558,6.0558,6.0558,6.0558 815 | YIE,6.0860,6.0860,6.0860,6.0860,6.0860,6.0860,6.0860 816 | KS5,6.1163,6.1163,6.1163,6.1163,6.1163,6.1163,6.1163 817 | FMZ,6.1468,6.1468,6.1468,6.1468,6.1468,6.1468,6.1468 818 | R7J,6.1775,6.1775,6.1775,6.1775,6.1775,6.1775,6.1775 819 | FMR,6.2083,6.2083,6.2083,6.2083,6.2083,6.2083,6.2083 820 | FMP,6.2393,6.2393,6.2393,6.2393,6.2393,6.2393,6.2393 821 | YIW,6.2704,6.2704,6.2704,6.2704,6.2704,6.2704,6.2704 822 | CBT,6.3016,6.3016,6.3016,6.3016,6.3016,6.3016,6.3016 823 | 85I,6.3331,6.3331,6.3331,6.3331,6.3331,6.3331,6.3331 824 | WQU,6.3647,6.3647,6.3647,6.3647,6.3647,6.3647,6.3647 825 | CBZ,6.3964,6.3964,6.3964,6.3964,6.3964,6.3964,6.3964 826 | 4SV,6.4283,6.4283,6.4283,6.4283,6.4283,6.4283,6.4283 827 | W2Y,6.4604,6.4604,6.4604,6.4604,6.4604,6.4604,6.4604 828 | FQO,6.4926,6.4926,6.4926,6.4926,6.4926,6.4926,6.4926 829 | CAP,6.5250,6.5250,6.5250,6.5250,6.5250,6.5250,6.5250 830 | BZQ,6.5575,6.5575,6.5575,6.5575,6.5575,6.5575,6.5575 831 | IBV,6.5902,6.5902,6.5902,6.5902,6.5902,6.5902,6.5902 832 | ZR5,6.6231,6.6231,6.6231,6.6231,6.6231,6.6231,6.6231 833 | 6TQ,6.6561,6.6561,6.6561,6.6561,6.6561,6.6561,6.6561 834 | 61K,6.6893,6.6893,6.6893,6.6893,6.6893,6.6893,6.6893 835 | BAQ,6.7227,6.7227,6.7227,6.7227,6.7227,6.7227,6.7227 836 | BXG,6.7562,6.7562,6.7562,6.7562,6.7562,6.7562,6.7562 837 | D4V,6.7899,6.7899,6.7899,6.7899,6.7899,6.7899,6.7899 838 | L4H,6.8238,6.8238,6.8238,6.8238,6.8238,6.8238,6.8238 839 | P5T,6.8578,6.8578,6.8578,6.8578,6.8578,6.8578,6.8578 840 | RSM,6.8920,6.8920,6.8920,6.8920,6.8920,6.8920,6.8920 841 | 5QO,6.9264,6.9264,6.9264,6.9264,6.9264,6.9264,6.9264 842 | 8LF,6.9609,6.9609,6.9609,6.9609,6.9609,6.9609,6.9609 843 | 2EW,6.9957,6.9957,6.9957,6.9957,6.9957,6.9957,6.9957 844 | MDW,7.0305,7.0305,7.0305,7.0305,7.0305,7.0305,7.0305 845 | F4F,7.0656,7.0656,7.0656,7.0656,7.0656,7.0656,7.0656 846 | 74J,7.1009,7.1009,7.1009,7.1009,7.1009,7.1009,7.1009 847 | 0GO,7.1363,7.1363,7.1363,7.1363,7.1363,7.1363,7.1363 848 | 6IO,7.1719,7.1719,7.1719,7.1719,7.1719,7.1719,7.1719 849 | W8P,7.2076,7.2076,7.2076,7.2076,7.2076,7.2076,7.2076 850 | 9H9,7.2436,7.2436,7.2436,7.2436,7.2436,7.2436,7.2436 851 | JEH,7.2797,7.2797,7.2797,7.2797,7.2797,7.2797,7.2797 852 | 7PF,7.3160,7.3160,7.3160,7.3160,7.3160,7.3160,7.3160 853 | MVM,7.3525,7.3525,7.3525,7.3525,7.3525,7.3525,7.3525 854 | B9Z,7.3892,7.3892,7.3892,7.3892,7.3892,7.3892,7.3892 855 | AOR,7.4260,7.4260,7.4260,7.4260,7.4260,7.4260,7.4260 856 | T0P,7.4631,7.4631,7.4631,7.4631,7.4631,7.4631,7.4631 857 | 2S7,7.5003,7.5003,7.5003,7.5003,7.5003,7.5003,7.5003 858 | XZK,7.5377,7.5377,7.5377,7.5377,7.5377,7.5377,7.5377 859 | SOT,7.5753,7.5753,7.5753,7.5753,7.5753,7.5753,7.5753 860 | 2SM,7.6131,7.6131,7.6131,7.6131,7.6131,7.6131,7.6131 861 | U5Z,7.6510,7.6510,7.6510,7.6510,7.6510,7.6510,7.6510 862 | 9HH,7.6892,7.6892,7.6892,7.6892,7.6892,7.6892,7.6892 863 | 49F,7.7276,7.7276,7.7276,7.7276,7.7276,7.7276,7.7276 864 | 3KY,7.7661,7.7661,7.7661,7.7661,7.7661,7.7661,7.7661 865 | 7P2,7.8048,7.8048,7.8048,7.8048,7.8048,7.8048,7.8048 866 | V74,7.8438,7.8438,7.8438,7.8438,7.8438,7.8438,7.8438 867 | ZN7,7.8829,7.8829,7.8829,7.8829,7.8829,7.8829,7.8829 868 | SFK,7.9222,7.9222,7.9222,7.9222,7.9222,7.9222,7.9222 869 | IPU,7.9617,7.9617,7.9617,7.9617,7.9617,7.9617,7.9617 870 | SM5,8.0014,8.0014,8.0014,8.0014,8.0014,8.0014,8.0014 871 | GQN,8.0413,8.0413,8.0413,8.0413,8.0413,8.0413,8.0413 872 | XLN,8.0814,8.0814,8.0814,8.0814,8.0814,8.0814,8.0814 873 | K36,8.1217,8.1217,8.1217,8.1217,8.1217,8.1217,8.1217 874 | KSG,8.1622,8.1622,8.1622,8.1622,8.1622,8.1622,8.1622 875 | GZ4,8.2030,8.2030,8.2030,8.2030,8.2030,8.2030,8.2030 876 | I4U,8.2439,8.2439,8.2439,8.2439,8.2439,8.2439,8.2439 877 | SH1,8.2850,8.2850,8.2850,8.2850,8.2850,8.2850,8.2850 878 | BMN,8.3263,8.3263,8.3263,8.3263,8.3263,8.3263,8.3263 879 | 0Y6,8.3678,8.3678,8.3678,8.3678,8.3678,8.3678,8.3678 880 | JTV,8.4096,8.4096,8.4096,8.4096,8.4096,8.4096,8.4096 881 | SZ8,8.4515,8.4515,8.4515,8.4515,8.4515,8.4515,8.4515 882 | BM1,8.4937,8.4937,8.4937,8.4937,8.4937,8.4937,8.4937 883 | 7BV,8.5360,8.5360,8.5360,8.5360,8.5360,8.5360,8.5360 884 | MGJ,8.5786,8.5786,8.5786,8.5786,8.5786,8.5786,8.5786 885 | GZC,8.6214,8.6214,8.6214,8.6214,8.6214,8.6214,8.6214 886 | 7BC,8.6644,8.6644,8.6644,8.6644,8.6644,8.6644,8.6644 887 | I47,8.7076,8.7076,8.7076,8.7076,8.7076,8.7076,8.7076 888 | 94H,8.7510,8.7510,8.7510,8.7510,8.7510,8.7510,8.7510 889 | R4A,8.7947,8.7947,8.7947,8.7947,8.7947,8.7947,8.7947 890 | HV0,8.8385,8.8385,8.8385,8.8385,8.8385,8.8385,8.8385 891 | R4Y,8.8826,8.8826,8.8826,8.8826,8.8826,8.8826,8.8826 892 | A32,8.9269,8.9269,8.9269,8.9269,8.9269,8.9269,8.9269 893 | PP8,8.9715,8.9715,8.9715,8.9715,8.9715,8.9715,8.9715 894 | TL5,9.0162,9.0162,9.0162,9.0162,9.0162,9.0162,9.0162 895 | QKJ,9.0612,9.0612,9.0612,9.0612,9.0612,9.0612,9.0612 896 | WPH,9.1064,9.1064,9.1064,9.1064,9.1064,9.1064,9.1064 897 | 4LE,9.1518,9.1518,9.1518,9.1518,9.1518,9.1518,9.1518 898 | 185,9.1974,9.1974,9.1974,9.1974,9.1974,9.1974,9.1974 899 | XO4,9.2433,9.2433,9.2433,9.2433,9.2433,9.2433,9.2433 900 | 97U,9.2894,9.2894,9.2894,9.2894,9.2894,9.2894,9.2894 901 | 4L6,9.3357,9.3357,9.3357,9.3357,9.3357,9.3357,9.3357 902 | KFS,9.3823,9.3823,9.3823,9.3823,9.3823,9.3823,9.3823 903 | 2KV,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 904 | 8MS,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 905 | WLP,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 906 | AEA,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 907 | WJ3,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 908 | VVD,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 909 | 2PR,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 910 | 3JV,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 911 | N6J,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 912 | RVS,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 913 | LBG,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 914 | 918,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 915 | LG0,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 916 | 5ZX,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 917 | OYL,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 918 | 7X7,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 919 | 11H,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 920 | 10R,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 921 | CQX,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 922 | LPH,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 923 | 6R1,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 924 | S5N,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 925 | 3KM,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 926 | T85,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 927 | KJR,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 928 | PJ4,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 929 | BRP,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 930 | DGB,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 931 | F73,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 932 | 3LW,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 933 | I00,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 934 | MH4,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 935 | FWS,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 936 | EWJ,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 937 | P8X,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 938 | IZL,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 939 | QHR,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 940 | C5R,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 941 | VQF,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 942 | 419,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 943 | BTG,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 944 | P26,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 945 | 3BO,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 946 | XNQ,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 947 | 339,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 948 | U7C,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 949 | 7OR,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 950 | G1M,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 951 | YA8,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 952 | HWF,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 953 | UTQ,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 954 | I1R,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 955 | 80P,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 956 | 0GH,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 957 | QEZ,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 958 | CGU,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 959 | V1Q,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 960 | DU0,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 961 | X0H,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 962 | J1X,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 963 | LND,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 964 | OBW,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 965 | 9K8,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 966 | T49,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 967 | LNP,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 968 | ET5,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 969 | OMK,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 970 | 45W,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 971 | 25Q,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 972 | XIA,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 973 | O3W,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 974 | DWH,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 975 | BBJ,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 976 | RNE,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 977 | SWV,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 978 | 14U,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 979 | TXI,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 980 | 3Y1,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 981 | 1SJ,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 982 | 2L4,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 983 | WJR,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 984 | RI4,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 985 | P5N,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 986 | ZJ0,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 987 | 6E0,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 988 | WRO,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 989 | PZ0,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 990 | A5L,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 991 | W7U,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 992 | ON1,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 993 | TE8,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 994 | LGH,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 995 | 07M,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 996 | 1O7,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 997 | 2EJ,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 998 | 60F,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 999 | GXF,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 1000 | 0W9,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 1001 | DXE,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000 1002 | -------------------------------------------------------------------------------- /data/csv/territory-definitions-table.csv: -------------------------------------------------------------------------------- 1 | county,county_code,territory,zipcode,town,area 2 | ALLEGANY,1,1502,21502,CRESAPTOWN,160 3 | ALLEGANY,1,1502,21502,CUMBERLAND,160 4 | ALLEGANY,1,1521,21521,BARTON,160 5 | ALLEGANY,1,1524,21524,CORRIGANVILLE,160 6 | ALLEGANY,1,1529,21529,ELLERSLIE,160 7 | ALLEGANY,1,1530,21530,FLINTSTONE,160 8 | ALLEGANY,1,1532,21532,FROSTBURG,160 9 | ALLEGANY,1,1539,21539,LONACONING,160 10 | ALLEGANY,1,1540,21540,LUKE,160 11 | ALLEGANY,1,1542,21542,CORRIGANVILLE,160 12 | ALLEGANY,1,1543,21543,MIDLOTHIAN,160 13 | ALLEGANY,1,1545,21545,MT SAVAGE,160 14 | ALLEGANY,1,1555,21555,OLDTOWN,160 15 | ALLEGANY,1,1557,21557,RAWLINGS,160 16 | ALLEGANY,1,1560,21560,SPRING GAP,160 17 | ALLEGANY,1,1562,21562,WESTERNPORT,160 18 | ALLEGANY,1,1766,21766,LITTLE ORLEANS,160 19 | ANNE ARUNDEL,2,701,20701,ANNAPOLIS JUNC,240 20 | ANNE ARUNDEL,2,711,20711,LOTHIAN,240 21 | ANNE ARUNDEL,2,714,20714,NORTH BEACH,240 22 | ANNE ARUNDEL,2,724,20724,LAUREL,240 23 | ANNE ARUNDEL,2,733,20733,CHURCHTON,240 24 | ANNE ARUNDEL,2,751,20751,DEALE,240 25 | ANNE ARUNDEL,2,754,20754,DUNKIRK,240 26 | ANNE ARUNDEL,2,755,20755,FORT MEADE,240 27 | ANNE ARUNDEL,2,755,20755,FT MEADE,240 28 | ANNE ARUNDEL,2,758,20758,FRIENDSHIP,240 29 | ANNE ARUNDEL,2,764,20764,SHADY SIDE,240 30 | ANNE ARUNDEL,2,765,20765,GALESVILLE,240 31 | ANNE ARUNDEL,2,776,20776,HARWOOD,240 32 | ANNE ARUNDEL,2,778,20778,WEST RIVER,240 33 | ANNE ARUNDEL,2,779,20779,TRACYS LANDING,240 34 | ANNE ARUNDEL,2,794,20794,JESSUP,240 35 | ANNE ARUNDEL,2,1012,21012,ARNOLD,240 36 | ANNE ARUNDEL,2,1032,21032,CROWNSVILLE,240 37 | ANNE ARUNDEL,2,1035,21035,DAVIDSONVILLE,240 38 | ANNE ARUNDEL,2,1037,21037,EDGEWATER,240 39 | ANNE ARUNDEL,2,1054,21054,GAMBRILLS,240 40 | ANNE ARUNDEL,2,1056,21056,GIBSON ISLAND,240 41 | ANNE ARUNDEL,2,1060,21060,GLEN BURNIE,240 42 | ANNE ARUNDEL,2,1061,21061,FERNDALE,240 43 | ANNE ARUNDEL,2,1061,21061,GLEN BURNIE,240 44 | ANNE ARUNDEL,2,1076,21076,HANOVER,240 45 | ANNE ARUNDEL,2,1077,21077,HARMANS,240 46 | ANNE ARUNDEL,2,1090,21090,LINTHICUM,240 47 | ANNE ARUNDEL,2,1090,21090,LINTHICUM HGTS,240 48 | ANNE ARUNDEL,2,1108,21108,MILLERSVILLE,240 49 | ANNE ARUNDEL,2,1113,21113,ODENTON,240 50 | ANNE ARUNDEL,2,1114,21114,CROFTON,240 51 | ANNE ARUNDEL,2,1122,21122,PASADENA,240 52 | ANNE ARUNDEL,2,1140,21140,RIVA,240 53 | ANNE ARUNDEL,2,1144,21144,SEVERN,240 54 | ANNE ARUNDEL,2,1146,21146,SEVERNA PARK,240 55 | ANNE ARUNDEL,2,1146,21146,SEVERNA PK,240 56 | ANNE ARUNDEL,2,1225,21225,BALTO,240 57 | ANNE ARUNDEL,2,1226,21226,BALTIMORE,240 58 | ANNE ARUNDEL,2,1240,21240,BALTIMORE,240 59 | ANNE ARUNDEL,2,1401,21401,ANNAPOLIS,240 60 | ANNE ARUNDEL,2,1402,21402,ANNAPOLIS,240 61 | ANNE ARUNDEL,2,1403,21403,ANNAPOLIS,240 62 | ANNE ARUNDEL,2,1405,21405,ANNAPOLIS,240 63 | ANNE ARUNDEL,2,1405,21405,SHERWOOD FOREST,240 64 | ANNE ARUNDEL,2,1409,21409,ANNAPOLIS,240 65 | BALTIMORE,3,1013,21013,BALDWIN,260 66 | BALTIMORE,3,1030,21030,COCKEYSVILLE,260 67 | BALTIMORE,3,1030,21030,HUNT VALLEY,260 68 | BALTIMORE,3,1031,21031,COCKEYSVILLE,260 69 | BALTIMORE,3,1043,21043,ELLICOTT CITY,260 70 | BALTIMORE,3,1051,21051,FORK,260 71 | BALTIMORE,3,1052,21052,FORT HOWARD,260 72 | BALTIMORE,3,1053,21053,FREELAND,260 73 | BALTIMORE,3,1057,21057,GLEN ARM,260 74 | BALTIMORE,3,1071,21071,GLYNDON,260 75 | BALTIMORE,3,1074,21074,HAMPSTEAD,260 76 | BALTIMORE,3,1075,21075,ELKRIDGE,260 77 | BALTIMORE,3,1082,21082,HYDES,260 78 | BALTIMORE,3,1085,21085,JOPPA,260 79 | BALTIMORE,3,1087,21087,BRADSHAW,260 80 | BALTIMORE,3,1093,21093,LTHRVL TIMONIUM,260 81 | BALTIMORE,3,1093,21093,LUTHERVILLE,260 82 | BALTIMORE,3,1102,21102,MANCHESTER,260 83 | BALTIMORE,3,1105,21105,MARYLAND LINE,260 84 | BALTIMORE,3,1111,21111,MONKTON,260 85 | BALTIMORE,3,1117,21117,OWINGS MILLS,260 86 | BALTIMORE,3,1120,21120,PARKTON,260 87 | BALTIMORE,3,1128,21128,PERRY HALL,260 88 | BALTIMORE,3,1131,21131,PHOENIX,260 89 | BALTIMORE,3,1133,21133,RANDALLSTOWN,260 90 | BALTIMORE,3,1136,21136,REISTERSTOWN,260 91 | BALTIMORE,3,1152,21152,GLENCOE,260 92 | BALTIMORE,3,1152,21152,SPARKS,260 93 | BALTIMORE,3,1153,21153,STEVENSON,260 94 | BALTIMORE,3,1155,21155,UPPERCO,260 95 | BALTIMORE,3,1156,21156,UPPER FALLS,260 96 | BALTIMORE,3,1161,21161,WHITE HALL,260 97 | BALTIMORE,3,1162,21162,WHITE MARSH,260 98 | BALTIMORE,3,1163,21163,WOODSTOCK,260 99 | BALTIMORE,3,1201,21201,BALTIMORE,260 100 | BALTIMORE,3,1202,21202,BALTIMORE,260 101 | BALTIMORE,3,1204,21204,BALTIMORE,260 102 | BALTIMORE,3,1204,21204,TOWSON,260 103 | BALTIMORE,3,1205,21205,BALTIMORE,260 104 | BALTIMORE,3,1206,21206,BALTIMORE,260 105 | BALTIMORE,3,1207,21207,BALTIMORE,260 106 | BALTIMORE,3,1208,21208,BALTIMORE,260 107 | BALTIMORE,3,1209,21209,BALTIMORE,260 108 | BALTIMORE,3,1210,21210,BALTIMORE,260 109 | BALTIMORE,3,1211,21211,BALTIMORE,260 110 | BALTIMORE,3,1212,21212,BALTIMORE,260 111 | BALTIMORE,3,1213,21213,BALTIMORE,260 112 | BALTIMORE,3,1214,21214,BALTIMORE,260 113 | BALTIMORE,3,1215,21215,BALTIMORE,260 114 | BALTIMORE,3,1216,21216,BALTIMORE,260 115 | BALTIMORE,3,1217,21217,BALTIMORE,260 116 | BALTIMORE,3,1218,21218,BALTIMORE,260 117 | BALTIMORE,3,1219,21219,BALTIMORE,260 118 | BALTIMORE,3,1219,21219,EDGEMERE,260 119 | BALTIMORE,3,1220,21220,BALTIMORE,260 120 | BALTIMORE,3,1220,21220,MIDDLE RIVER,260 121 | BALTIMORE,3,1221,21221,BALTIMORE,260 122 | BALTIMORE,3,1221,21221,ESSEX,260 123 | BALTIMORE,3,1222,21222,BALTIMORE,260 124 | BALTIMORE,3,1222,21222,DUNDALK,260 125 | BALTIMORE,3,1223,21223,BALTIMORE,260 126 | BALTIMORE,3,1224,21224,BALTIMORE,260 127 | BALTIMORE,3,1225,21225,BALTO,260 128 | BALTIMORE,3,1226,21226,BALTIMORE,260 129 | BALTIMORE,3,1227,21227,BALTIMORE,260 130 | BALTIMORE,3,1227,21227,ARBUTUS,260 131 | BALTIMORE,3,1228,21228,BALTIMORE,260 132 | BALTIMORE,3,1228,21228,CATONSVILLE,260 133 | BALTIMORE,3,1229,21229,BALTIMORE,260 134 | BALTIMORE,3,1230,21230,BALTIMORE,260 135 | BALTIMORE,3,1231,21231,BALTIMORE,260 136 | BALTIMORE,3,1233,21233,BALTIMORE,260 137 | BALTIMORE,3,1234,21234,BALTIMORE,260 138 | BALTIMORE,3,1236,21236,BALTIMORE,260 139 | BALTIMORE,3,1236,21236,PERRY HALL,260 140 | BALTIMORE,3,1237,21237,BALTIMORE,260 141 | BALTIMORE,3,1237,21237,ROSEDALE,260 142 | BALTIMORE,3,1239,21239,BALTIMORE,260 143 | BALTIMORE,3,1244,21244,BALTIMORE,260 144 | BALTIMORE,3,1250,21250,CATONSVILLE,260 145 | BALTIMORE,3,1250,21250,BALTIMORE,260 146 | BALTIMORE,3,1252,21252,TOWSON,260 147 | BALTIMORE,3,1286,21286,BALTIMORE,260 148 | BALTIMORE,3,1286,21286,TOWSON,260 149 | CALVERT,4,615,20615,BROOMES ISLAND,230 150 | CALVERT,4,629,20629,DOWELL,230 151 | CALVERT,4,639,20639,HUNTINGTOWN,230 152 | CALVERT,4,657,20657,LUSBY,230 153 | CALVERT,4,676,20676,PORT REPUBLIC,230 154 | CALVERT,4,678,20678,PR FREDERICK,230 155 | CALVERT,4,685,20685,ST LEONARD,230 156 | CALVERT,4,688,20688,SOLOMONS,230 157 | CALVERT,4,689,20689,SUNDERLAND,230 158 | CALVERT,4,714,20714,NORTH BEACH,230 159 | CALVERT,4,732,20732,CHESAPEAKE BCH,230 160 | CALVERT,4,736,20736,OWINGS,230 161 | CALVERT,4,754,20754,DUNKIRK,230 162 | CAROLINE,5,1629,21629,DENTON,330 163 | CAROLINE,5,1632,21632,FEDERALSBURG,330 164 | CAROLINE,5,1636,21636,GOLDSBORO,330 165 | CAROLINE,5,1639,21639,GREENSBORO,330 166 | CAROLINE,5,1640,21640,HENDERSON,330 167 | CAROLINE,5,1641,21641,HILLSBORO,330 168 | CAROLINE,5,1649,21649,MARYDEL,330 169 | CAROLINE,5,1655,21655,PRESTON,330 170 | CAROLINE,5,1660,21660,RIDGELY,330 171 | CARROLL,6,1048,21048,FINKSBURG,270 172 | CARROLL,6,1074,21074,HAMPSTEAD,270 173 | CARROLL,6,1102,21102,MANCHESTER,270 174 | CARROLL,6,1104,21104,MARRIOTTSVILLE,270 175 | CARROLL,6,1136,21136,REISTERSTOWN,270 176 | CARROLL,6,1155,21155,UPPERCO,270 177 | CARROLL,6,1157,21157,WESTMINSTER,270 178 | CARROLL,6,1158,21158,WESTMINSTER,270 179 | CARROLL,6,1757,21757,KEYMAR,270 180 | CARROLL,6,1776,21776,NEW WINDSOR,270 181 | CARROLL,6,1784,21784,ELDERSBURG,270 182 | CARROLL,6,1787,21787,TANEYTOWN,270 183 | CARROLL,6,1791,21791,UNION BRIDGE,270 184 | CARROLL,6,1797,21797,WOODBINE,270 185 | CECIL,7,1901,21901,NORTH EAST,290 186 | CECIL,7,1903,21903,PERRYVILLE,290 187 | CECIL,7,1904,21904,PORT DEPOSIT,290 188 | CECIL,7,1911,21911,RISING SUN,290 189 | CECIL,7,1912,21912,WARWICK,290 190 | CECIL,7,1913,21913,CECILTON,290 191 | CECIL,7,1914,21914,CHARLESTOWN,290 192 | CECIL,7,1915,21915,CHESAPEAKE CITY,290 193 | CECIL,7,1917,21917,COLORA,290 194 | CECIL,7,1918,21918,CONOWINGO,290 195 | CECIL,7,1919,21919,EARLEVILLE,290 196 | CECIL,7,1920,21920,ELK MILLS,290 197 | CECIL,7,1921,21921,ELKTON,290 198 | CECIL,7,1930,21930,GEORGETOWN,290 199 | CHARLES,8,601,20601,WALDORF,210 200 | CHARLES,8,602,20602,WALDORF,210 201 | CHARLES,8,603,20603,WALDORF,210 202 | CHARLES,8,611,20611,BEL ALTON,210 203 | CHARLES,8,612,20612,BENEDICT,210 204 | CHARLES,8,613,20613,BRANDYWINE,210 205 | CHARLES,8,616,20616,BRYANS ROAD,210 206 | CHARLES,8,617,20617,BRYANTOWN,210 207 | CHARLES,8,622,20622,CHARLOTTE HALL,210 208 | CHARLES,8,625,20625,COBB ISLAND,210 209 | CHARLES,8,632,20632,FAULKNER,210 210 | CHARLES,8,637,20637,HUGHESVILLE,210 211 | CHARLES,8,640,20640,INDIAN HEAD,210 212 | CHARLES,8,645,20645,ISSUE,210 213 | CHARLES,8,646,20646,LA PLATA,210 214 | CHARLES,8,658,20658,MARBURY,210 215 | CHARLES,8,659,20659,MECHANICSVILLE,210 216 | CHARLES,8,662,20662,NANJEMOY,210 217 | CHARLES,8,664,20664,NEWBURG,210 218 | CHARLES,8,675,20675,POMFRET,210 219 | CHARLES,8,677,20677,PORT TOBACCO,210 220 | CHARLES,8,693,20693,WELCOME,210 221 | CHARLES,8,695,20695,WHITE PLAINS,210 222 | DORCHESTER,9,1613,21613,CAMBRIDGE,340 223 | DORCHESTER,9,1622,21622,CHURCH CREEK,340 224 | DORCHESTER,9,1626,21626,CRAPO,340 225 | DORCHESTER,9,1627,21627,CROCHERON,340 226 | DORCHESTER,9,1631,21631,EAST NEW MARKET,340 227 | DORCHESTER,9,1634,21634,FISHING CREEK,340 228 | DORCHESTER,9,1643,21643,HURLOCK,340 229 | DORCHESTER,9,1648,21648,MADISON,340 230 | DORCHESTER,9,1659,21659,RHODESDALE,340 231 | DORCHESTER,9,1664,21664,SECRETARY,340 232 | DORCHESTER,9,1669,21669,TAYLORS ISLAND,340 233 | DORCHESTER,9,1672,21672,TODDVILLE,340 234 | DORCHESTER,9,1675,21675,WINGATE,340 235 | DORCHESTER,9,1677,21677,WOOLFORD,340 236 | DORCHESTER,9,1835,21835,LINKWOOD,340 237 | DORCHESTER,9,1869,21869,VIENNA,340 238 | FREDERICK,10,842,20842,DICKERSON,180 239 | FREDERICK,10,871,20871,CLARKSBURG,180 240 | FREDERICK,10,1701,21701,FREDERICK,180 241 | FREDERICK,10,1701,21701,FT DETRICK,180 242 | FREDERICK,10,1702,21702,FREDERICK,180 243 | FREDERICK,10,1703,21703,FREDERICK,180 244 | FREDERICK,10,1704,21704,FREDERICK,180 245 | FREDERICK,10,1710,21710,ADAMSTOWN,180 246 | FREDERICK,10,1714,21714,BRADDOCK HEIGHTS,180 247 | FREDERICK,10,1716,21716,BRUNSWICK,180 248 | FREDERICK,10,1717,21717,BUCKEYSTOWN,180 249 | FREDERICK,10,1718,21718,BURKITTSVILLE,180 250 | FREDERICK,10,1719,21719,CASCADE,180 251 | FREDERICK,10,1727,21727,EMMITSBURG,180 252 | FREDERICK,10,1754,21754,IJAMSVILLE,180 253 | FREDERICK,10,1755,21755,JEFFERSON,180 254 | FREDERICK,10,1757,21757,KEYMAR,180 255 | FREDERICK,10,1758,21758,KNOXVILLE,180 256 | FREDERICK,10,1759,21759,LADIESBURG,180 257 | FREDERICK,10,1762,21762,LIBERTYTOWN,180 258 | FREDERICK,10,1769,21769,MIDDLETOWN,180 259 | FREDERICK,10,1770,21770,MONROVIA,180 260 | FREDERICK,10,1771,21771,MOUNT AIRY,180 261 | FREDERICK,10,1773,21773,MYERSVILLE,180 262 | FREDERICK,10,1774,21774,NEW MARKET,180 263 | FREDERICK,10,1776,21776,NEW WINDSOR,180 264 | FREDERICK,10,1777,21777,POINT OF ROCKS,180 265 | FREDERICK,10,1777,21777,PT OF ROCKS,180 266 | FREDERICK,10,1778,21778,ROCKY RIDGE,180 267 | FREDERICK,10,1780,21780,SABILLASVILLE,180 268 | FREDERICK,10,1783,21783,SMITHSBURG,180 269 | FREDERICK,10,1787,21787,TANEYTOWN,180 270 | FREDERICK,10,1788,21788,THURMONT,180 271 | FREDERICK,10,1790,21790,TUSCARORA,180 272 | FREDERICK,10,1791,21791,UNION BRIDGE,180 273 | FREDERICK,10,1793,21793,WALKERSVILLE,180 274 | FREDERICK,10,1798,21798,WOODSBORO,180 275 | GARRETT,11,1520,21520,ACCIDENT,150 276 | GARRETT,11,1521,21521,BARTON,150 277 | GARRETT,11,1522,21522,BITTINGER,150 278 | GARRETT,11,1523,21523,BLOOMINGTON,150 279 | GARRETT,11,1531,21531,FRIENDSVILLE,150 280 | GARRETT,11,1532,21532,FROSTBURG,150 281 | GARRETT,11,1536,21536,GRANTSVILLE,150 282 | GARRETT,11,1538,21538,KITZMILLER,150 283 | GARRETT,11,1539,21539,LONACONING,150 284 | GARRETT,11,1541,21541,MCHENRY,150 285 | GARRETT,11,1550,21550,DEER PARK,150 286 | GARRETT,11,1550,21550,MT LAKE PARK,150 287 | GARRETT,11,1561,21561,SWANTON,150 288 | HARFORD,12,1001,21001,ABERDEEN,280 289 | HARFORD,12,1005,21005,ABERDEEN PR GR,280 290 | HARFORD,12,1009,21009,ABINGDON,280 291 | HARFORD,12,1010,21010,GUNPOWDER,280 292 | HARFORD,12,1013,21013,BALDWIN,280 293 | HARFORD,12,1014,21014,BEL AIR,280 294 | HARFORD,12,1014,21014,FAIRMONT,280 295 | HARFORD,12,1015,21015,BEL AIR,280 296 | HARFORD,12,1017,21017,BELCAMP,280 297 | HARFORD,12,1028,21028,CHURCHVILLE,280 298 | HARFORD,12,1034,21034,DARLINGTON,280 299 | HARFORD,12,1040,21040,EDGEWOOD,280 300 | HARFORD,12,1047,21047,FALLSTON,280 301 | HARFORD,12,1050,21050,FOREST HILL,280 302 | HARFORD,12,1050,21050,HICKORY,280 303 | HARFORD,12,1078,21078,HAVRE DE GRACE,280 304 | HARFORD,12,1084,21084,JARRETTSVILLE,280 305 | HARFORD,12,1085,21085,JOPPA,280 306 | HARFORD,12,1087,21087,BRADSHAW,280 307 | HARFORD,12,1111,21111,MONKTON,280 308 | HARFORD,12,1130,21130,PERRYMAN,280 309 | HARFORD,12,1132,21132,PYLESVILLE,280 310 | HARFORD,12,1154,21154,STREET,280 311 | HARFORD,12,1160,21160,WHITEFORD,280 312 | HARFORD,12,1161,21161,WHITE HALL,280 313 | HOWARD,13,701,20701,ANNAPOLIS JUNC,250 314 | HOWARD,13,723,20723,LAUREL,250 315 | HOWARD,13,759,20759,FULTON,250 316 | HOWARD,13,763,20763,SAVAGE,250 317 | HOWARD,13,777,20777,HIGHLAND,250 318 | HOWARD,13,794,20794,JESSUP,250 319 | HOWARD,13,833,20833,BROOKEVILLE,250 320 | HOWARD,13,1029,21029,CLARKSVILLE,250 321 | HOWARD,13,1036,21036,DAYTON,250 322 | HOWARD,13,1042,21042,ELLICOTT CITY,250 323 | HOWARD,13,1043,21043,ELLICOTT CITY,250 324 | HOWARD,13,1044,21044,COLUMBIA,250 325 | HOWARD,13,1045,21045,COLUMBIA,250 326 | HOWARD,13,1046,21046,COLUMBIA,250 327 | HOWARD,13,1075,21075,ELKRIDGE,250 328 | HOWARD,13,1076,21076,HANOVER,250 329 | HOWARD,13,1104,21104,MARRIOTTSVILLE,250 330 | HOWARD,13,1163,21163,WOODSTOCK,250 331 | HOWARD,13,1723,21723,COOKSVILLE,250 332 | HOWARD,13,1723,21723,LISBON,250 333 | HOWARD,13,1737,21737,GLENELG,250 334 | HOWARD,13,1738,21738,GLENWOOD,250 335 | HOWARD,13,1765,21765,LISBON,250 336 | HOWARD,13,1784,21784,ELDERSBURG,250 337 | HOWARD,13,1794,21794,W FRIENDSHIP,250 338 | HOWARD,13,1794,21794,WEST FRIENDSHIP,250 339 | HOWARD,13,1797,21797,WOODBINE,250 340 | KENT,14,1610,21610,BETTERTON,300 341 | KENT,14,1620,21620,CHESTERTOWN,300 342 | KENT,14,1635,21635,GALENA,300 343 | KENT,14,1645,21645,KENNEDYVILLE,300 344 | KENT,14,1650,21650,MASSEY,300 345 | KENT,14,1651,21651,MILLINGTON,300 346 | KENT,14,1661,21661,ROCK HALL,300 347 | KENT,14,1667,21667,STILL POND,300 348 | KENT,14,1678,21678,WORTON,300 349 | MONTGOMERY,15,707,20707,LAUREL,190 350 | MONTGOMERY,15,812,20812,GLEN ECHO,190 351 | MONTGOMERY,15,814,20814,BETHESDA,190 352 | MONTGOMERY,15,815,20815,BETHESDA,190 353 | MONTGOMERY,15,815,20815,CHEVY CHASE,190 354 | MONTGOMERY,15,816,20816,BETHESDA,190 355 | MONTGOMERY,15,817,20817,BETHESDA,190 356 | MONTGOMERY,15,818,20818,CABIN JOHN,190 357 | MONTGOMERY,15,832,20832,OLNEY,190 358 | MONTGOMERY,15,833,20833,BROOKEVILLE,190 359 | MONTGOMERY,15,837,20837,POOLESVILLE,190 360 | MONTGOMERY,15,838,20838,BARNESVILLE,190 361 | MONTGOMERY,15,839,20839,BEALLSVILLE,190 362 | MONTGOMERY,15,841,20841,BOYDS,190 363 | MONTGOMERY,15,842,20842,DICKERSON,190 364 | MONTGOMERY,15,850,20850,ROCKVILLE,190 365 | MONTGOMERY,15,851,20851,ROCKVILLE,190 366 | MONTGOMERY,15,852,20852,ROCKVILLE,190 367 | MONTGOMERY,15,853,20853,ROCKVILLE,190 368 | MONTGOMERY,15,854,20854,POTOMAC,190 369 | MONTGOMERY,15,854,20854,ROCKVILLE,190 370 | MONTGOMERY,15,855,20855,DERWOOD,190 371 | MONTGOMERY,15,855,20855,ROCKVILLE,190 372 | MONTGOMERY,15,860,20860,SANDY SPRING,190 373 | MONTGOMERY,15,861,20861,ASHTON,190 374 | MONTGOMERY,15,862,20862,BRINKLOW,190 375 | MONTGOMERY,15,866,20866,BURTONSVILLE,190 376 | MONTGOMERY,15,868,20868,SPENCERVILLE,190 377 | MONTGOMERY,15,871,20871,CLARKSBURG,190 378 | MONTGOMERY,15,872,20872,DAMASCUS,190 379 | MONTGOMERY,15,874,20874,GERMANTOWN,190 380 | MONTGOMERY,15,876,20876,GERMANTOWN,190 381 | MONTGOMERY,15,877,20877,DARNESTOWN,190 382 | MONTGOMERY,15,877,20877,GAITHERSBURG,190 383 | MONTGOMERY,15,878,20878,DARNESTOWN,190 384 | MONTGOMERY,15,878,20878,GAITHERSBURG,190 385 | MONTGOMERY,15,879,20879,GAITHERSBURG,190 386 | MONTGOMERY,15,879,20879,LAYTONSVILLE,190 387 | MONTGOMERY,15,880,20880,WASHINGTON GROVE,190 388 | MONTGOMERY,15,882,20882,GAITHERSBURG,190 389 | MONTGOMERY,15,886,20886,MONTGOMERY VLG,190 390 | MONTGOMERY,15,889,20889,BETHESDA,190 391 | MONTGOMERY,15,895,20895,KENSINGTON,190 392 | MONTGOMERY,15,896,20896,GARRETT PARK,190 393 | MONTGOMERY,15,901,20901,SILVER SPRING,190 394 | MONTGOMERY,15,901,20901,WHEATON,190 395 | MONTGOMERY,15,902,20902,SILVER SPRING,190 396 | MONTGOMERY,15,902,20902,WHEATON,190 397 | MONTGOMERY,15,903,20903,SILVER SPRING,190 398 | MONTGOMERY,15,904,20904,COLESVILLE,190 399 | MONTGOMERY,15,904,20904,SILVER SPRING,190 400 | MONTGOMERY,15,905,20905,SILVER SPRING,190 401 | MONTGOMERY,15,905,20905,WHEATON,190 402 | MONTGOMERY,15,906,20906,SILVER SPRING,190 403 | MONTGOMERY,15,906,20906,WHEATON,190 404 | MONTGOMERY,15,910,20910,SILVER SPRING,190 405 | MONTGOMERY,15,912,20912,TAKOMA PARK,190 406 | MONTGOMERY,15,1771,21771,MOUNT AIRY,190 407 | PRINCE GEORGES,16,601,20601,WALDORF,200 408 | PRINCE GEORGES,16,607,20607,ACCOKEEK,200 409 | PRINCE GEORGES,16,608,20608,AQUASCO,200 410 | PRINCE GEORGES,16,613,20613,BRANDYWINE,200 411 | PRINCE GEORGES,16,623,20623,CHELTENHAM,200 412 | PRINCE GEORGES,16,705,20705,BELTSVILLE,200 413 | PRINCE GEORGES,16,706,20706,LANHAM,200 414 | PRINCE GEORGES,16,706,20706,GLENARDEN,200 415 | PRINCE GEORGES,16,707,20707,LAUREL,200 416 | PRINCE GEORGES,16,708,20708,LAUREL,200 417 | PRINCE GEORGES,16,710,20710,BLADENSBURG,200 418 | PRINCE GEORGES,16,712,20712,MT RAINIER,200 419 | PRINCE GEORGES,16,715,20715,BOWIE,200 420 | PRINCE GEORGES,16,716,20716,BOWIE,200 421 | PRINCE GEORGES,16,716,20716,MITCHELLVILLE,200 422 | PRINCE GEORGES,16,720,20720,BOWIE,200 423 | PRINCE GEORGES,16,721,20721,BOWIE,200 424 | PRINCE GEORGES,16,721,20721,MITCHELLVILLE,200 425 | PRINCE GEORGES,16,722,20722,BRENTWOOD,200 426 | PRINCE GEORGES,16,722,20722,COLMAR MANOR,200 427 | PRINCE GEORGES,16,735,20735,CLINTON,200 428 | PRINCE GEORGES,16,737,20737,E RIVERDALE,200 429 | PRINCE GEORGES,16,737,20737,EAST RIVERDALE,200 430 | PRINCE GEORGES,16,740,20740,BERWYN HEIGHTS,200 431 | PRINCE GEORGES,16,740,20740,COLLEGE PARK,200 432 | PRINCE GEORGES,16,742,20742,COLLEGE PARK,200 433 | PRINCE GEORGES,16,743,20743,CAPITAL HEIGHTS,200 434 | PRINCE GEORGES,16,743,20743,CAPITAL HGHTS,200 435 | PRINCE GEORGES,16,744,20744,FORT WASH,200 436 | PRINCE GEORGES,16,744,20744,FORT WASHINGTON,200 437 | PRINCE GEORGES,16,745,20745,FOREST HEIGHTS,200 438 | PRINCE GEORGES,16,745,20745,OXON HILL,200 439 | PRINCE GEORGES,16,746,20746,CAMP SPRINGS,200 440 | PRINCE GEORGES,16,746,20746,SUITLAND,200 441 | PRINCE GEORGES,16,747,20747,DISTRICT HEIGHTS,200 442 | PRINCE GEORGES,16,748,20748,CAMP SPRINGS,200 443 | PRINCE GEORGES,16,748,20748,HILLCREST HGTS,200 444 | PRINCE GEORGES,16,752,20752,SUITLAND,200 445 | PRINCE GEORGES,16,762,20762,ANDREWS AFB,200 446 | PRINCE GEORGES,16,769,20769,GLENN DALE,200 447 | PRINCE GEORGES,16,770,20770,GREENBELT,200 448 | PRINCE GEORGES,16,772,20772,UPPER MARLBORO,200 449 | PRINCE GEORGES,16,774,20774,LARGO,200 450 | PRINCE GEORGES,16,774,20774,SPRINGDALE,200 451 | PRINCE GEORGES,16,781,20781,EDMONSTON,200 452 | PRINCE GEORGES,16,781,20781,HYATTSVILLE,200 453 | PRINCE GEORGES,16,782,20782,HYATTSVILLE,200 454 | PRINCE GEORGES,16,782,20782,CHILLUM,200 455 | PRINCE GEORGES,16,783,20783,ADELPHI,200 456 | PRINCE GEORGES,16,783,20783,CHILLUM,200 457 | PRINCE GEORGES,16,784,20784,HYATTSVILLE,200 458 | PRINCE GEORGES,16,784,20784,LANDOVER,200 459 | PRINCE GEORGES,16,785,20785,CHEVERLY,200 460 | PRINCE GEORGES,16,785,20785,HYATTSVILLE,200 461 | PRINCE GEORGES,16,903,20903,SILVER SPRING,200 462 | PRINCE GEORGES,16,912,20912,TAKOMA PARK,200 463 | QUEEN ANNES,17,1607,21607,BARCLAY,310 464 | QUEEN ANNES,17,1617,21617,CENTREVILLE,310 465 | QUEEN ANNES,17,1619,21619,CHESTER,310 466 | QUEEN ANNES,17,1620,21620,CHESTERTOWN,310 467 | QUEEN ANNES,17,1623,21623,CHURCH HILL,310 468 | QUEEN ANNES,17,1628,21628,CRUMPTON,310 469 | QUEEN ANNES,17,1638,21638,GRASONVILLE,310 470 | QUEEN ANNES,17,1644,21644,INGLESIDE,310 471 | QUEEN ANNES,17,1651,21651,MILLINGTON,310 472 | QUEEN ANNES,17,1657,21657,QUEEN ANNE,310 473 | QUEEN ANNES,17,1658,21658,QUEENSTOWN,310 474 | QUEEN ANNES,17,1666,21666,STEVENSVILLE,310 475 | QUEEN ANNES,17,1668,21668,SUDLERSVILLE,310 476 | QUEEN ANNES,17,1679,21679,WYE MILLS,310 477 | SAINT MARYS,18,606,20606,ABELL,220 478 | SAINT MARYS,18,609,20609,AVENUE,220 479 | SAINT MARYS,18,618,20618,BUSHWOOD,220 480 | SAINT MARYS,18,619,20619,CALIFORNIA,220 481 | SAINT MARYS,18,620,20620,CALLAWAY,220 482 | SAINT MARYS,18,620,20620,ST MARY,220 483 | SAINT MARYS,18,621,20621,CHAPTICO,220 484 | SAINT MARYS,18,622,20622,CHARLOTTE HALL,220 485 | SAINT MARYS,18,624,20624,CLEMENTS,220 486 | SAINT MARYS,18,626,20626,COLTONS POINT,220 487 | SAINT MARYS,18,628,20628,DAMERON,220 488 | SAINT MARYS,18,630,20630,DRAYDEN,220 489 | SAINT MARYS,18,634,20634,GREAT MILLS,220 490 | SAINT MARYS,18,636,20636,HOLLYWOOD,220 491 | SAINT MARYS,18,650,20650,LEONARDTOWN,220 492 | SAINT MARYS,18,653,20653,LEXINGTON PARK,220 493 | SAINT MARYS,18,653,20653,LEXINGTON PK,220 494 | SAINT MARYS,18,656,20656,LOVEVILLE,220 495 | SAINT MARYS,18,659,20659,MECHANICSVILLE,220 496 | SAINT MARYS,18,667,20667,PARK HALL,220 497 | SAINT MARYS,18,670,20670,NAS PAT RIVER,220 498 | SAINT MARYS,18,670,20670,PATUXENT RIVER,220 499 | SAINT MARYS,18,674,20674,PINEY POINT,220 500 | SAINT MARYS,18,680,20680,RIDGE,220 501 | SAINT MARYS,18,684,20684,ST INIGOES,220 502 | SAINT MARYS,18,687,20687,SCOTLAND,220 503 | SAINT MARYS,18,690,20690,TALL TIMBERS,220 504 | SAINT MARYS,18,692,20692,VALLEY LEE,220 505 | SOMERSET,19,1817,21817,CRISFIELD,360 506 | SOMERSET,19,1821,21821,CHANCE,360 507 | SOMERSET,19,1821,21821,DAMES QUARTER,360 508 | SOMERSET,19,1822,21822,EDEN,360 509 | SOMERSET,19,1824,21824,EWELL,360 510 | SOMERSET,19,1836,21836,MANOKIN,360 511 | SOMERSET,19,1838,21838,MARION,360 512 | SOMERSET,19,1838,21838,MARION STATION,360 513 | SOMERSET,19,1851,21851,POCOMOKE,360 514 | SOMERSET,19,1853,21853,PRINCESS ANNE,360 515 | SOMERSET,19,1866,21866,TYLERTON,360 516 | SOMERSET,19,1867,21867,UPPER FAIRMOUNT,360 517 | SOMERSET,19,1871,21871,FAIRMOUNT,360 518 | SOMERSET,19,1871,21871,WESTOVER,360 519 | TALBOT,20,1601,21601,EASTON,320 520 | TALBOT,20,1612,21612,BOZMAN,320 521 | TALBOT,20,1624,21624,CLAIBORNE,320 522 | TALBOT,20,1625,21625,CORDOVA,320 523 | TALBOT,20,1647,21647,MCDANIEL,320 524 | TALBOT,20,1652,21652,NEAVITT,320 525 | TALBOT,20,1653,21653,NEWCOMB,320 526 | TALBOT,20,1654,21654,OXFORD,320 527 | TALBOT,20,1657,21657,QUEEN ANNE,320 528 | TALBOT,20,1662,21662,ROYAL OAK,320 529 | TALBOT,20,1663,21663,ST MICHAELS,320 530 | TALBOT,20,1665,21665,SHERWOOD,320 531 | TALBOT,20,1671,21671,TILGHMAN,320 532 | TALBOT,20,1673,21673,TRAPPE,320 533 | TALBOT,20,1676,21676,WITTMAN,320 534 | TALBOT,20,1679,21679,WYE MILLS,320 535 | WASHINGTON,21,1711,21711,BIG POOL,170 536 | WASHINGTON,21,1713,21713,BOONSBORO,170 537 | WASHINGTON,21,1715,21715,BROWNSVILLE,170 538 | WASHINGTON,21,1719,21719,CASCADE,170 539 | WASHINGTON,21,1722,21722,BIG SPRING,170 540 | WASHINGTON,21,1722,21722,CLEAR SPRING,170 541 | WASHINGTON,21,1733,21733,FAIRPLAY,170 542 | WASHINGTON,21,1734,21734,FUNKSTOWN,170 543 | WASHINGTON,21,1740,21740,HAGERSTOWN,170 544 | WASHINGTON,21,1742,21742,HAGERSTOWN,170 545 | WASHINGTON,21,1750,21750,HANCOCK,170 546 | WASHINGTON,21,1756,21756,KEEDYSVILLE,170 547 | WASHINGTON,21,1758,21758,KNOXVILLE,170 548 | WASHINGTON,21,1767,21767,MAUGANSVILLE,170 549 | WASHINGTON,21,1779,21779,GAPLAND,170 550 | WASHINGTON,21,1779,21779,ROHRERSVILLE,170 551 | WASHINGTON,21,1780,21780,SABILLASVILLE,170 552 | WASHINGTON,21,1782,21782,SHARPSBURG,170 553 | WASHINGTON,21,1783,21783,SMITHSBURG,170 554 | WASHINGTON,21,1795,21795,WILLIAMSPORT,170 555 | WICOMICO,22,1801,21801,SALISBURY,350 556 | WICOMICO,22,1804,21804,SALISBURY,350 557 | WICOMICO,22,1810,21810,ALLEN,350 558 | WICOMICO,22,1814,21814,BIVALVE,350 559 | WICOMICO,22,1822,21822,EDEN,350 560 | WICOMICO,22,1826,21826,FRUITLAND,350 561 | WICOMICO,22,1830,21830,HEBRON,350 562 | WICOMICO,22,1837,21837,MARDELA,350 563 | WICOMICO,22,1837,21837,MARDELA SPGS,350 564 | WICOMICO,22,1840,21840,NANTICOKE,350 565 | WICOMICO,22,1849,21849,PARSONSBURG,350 566 | WICOMICO,22,1850,21850,PITTSVILLE,350 567 | WICOMICO,22,1856,21856,QUANTICO,350 568 | WICOMICO,22,1861,21861,SHARPTOWN,350 569 | WICOMICO,22,1865,21865,TYASKIN,350 570 | WICOMICO,22,1874,21874,WILLARDS,350 571 | WICOMICO,22,1875,21875,DELMAR,350 572 | WORCHESTER,23,1811,21811,BERLIN,370 573 | WORCHESTER,23,1813,21813,BISHOPVILLE,370 574 | WORCHESTER,23,1829,21829,GIRDLETREE,370 575 | WORCHESTER,23,1841,21841,NEWARK,370 576 | WORCHESTER,23,1842,21842,OCEAN CITY,370 577 | WORCHESTER,23,1851,21851,POCOMOKE,370 578 | WORCHESTER,23,1862,21862,SHOWELL,370 579 | WORCHESTER,23,1863,21863,SNOW HILL,370 580 | WORCHESTER,23,1864,21864,STOCKTON,370 581 | WORCHESTER,23,1872,21872,WHALEYSVILLE,370 582 | -------------------------------------------------------------------------------- /data/pdf/cgr-definitions-table.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/the-markup/investigation-allstates-algorithm/349c4f260852e9575d5892df70a2a48413471ec7/data/pdf/cgr-definitions-table.pdf -------------------------------------------------------------------------------- /data/pdf/cgr-premiums-table.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/the-markup/investigation-allstates-algorithm/349c4f260852e9575d5892df70a2a48413471ec7/data/pdf/cgr-premiums-table.pdf -------------------------------------------------------------------------------- /data/pdf/territory-definitions-table.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/the-markup/investigation-allstates-algorithm/349c4f260852e9575d5892df70a2a48413471ec7/data/pdf/territory-definitions-table.pdf -------------------------------------------------------------------------------- /py/table-extractor.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Table Extractor\n", 8 | "\n", 9 | "This script takes pdfs of tables and turns them into usable tables." 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": null, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "from collections import OrderedDict\n", 19 | "from datetime import datetime\n", 20 | "import csv, sys, os, io\n", 21 | "\n", 22 | "import pyparsing as pp" 23 | ] 24 | }, 25 | { 26 | "cell_type": "markdown", 27 | "metadata": {}, 28 | "source": [ 29 | "## Functions & Parser Definitions" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": null, 35 | "metadata": {}, 36 | "outputs": [], 37 | "source": [ 38 | "def processXML(xml_name, out_name, row_description, header=\"\"):\n", 39 | " table = pp.OneOrMore(pp.Group(row_description))\n", 40 | " with io.open(xml_name, 'r', encoding='utf8') as xml_file, io.open(out_name, 'w') as out_file:\n", 41 | " writer = csv.writer(out_file)\n", 42 | " print(writer)\n", 43 | " if header is not \"\":\n", 44 | " print(header)\n", 45 | " writer.writerow(header)\n", 46 | " data = xml_file.read()\n", 47 | " for result, start, end in table.scanString(data):\n", 48 | " for row in result:\n", 49 | " writer.writerow(row)" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": null, 55 | "metadata": {}, 56 | "outputs": [], 57 | "source": [ 58 | "decimals = pp.Combine(\n", 59 | " pp.Word(pp.nums, max=3)\n", 60 | " + pp.ZeroOrMore(pp.Suppress(\",\") + pp.Word(pp.nums, max=3))\n", 61 | " + pp.ZeroOrMore(\".\" + pp.Word(pp.nums))\n", 62 | ")\n", 63 | "pct = pp.Combine(pp.Optional(\"-\") + decimals + pp.Suppress(\"%\"))\n", 64 | "dollar = pp.Suppress(\"$\") + decimals\n", 65 | "date = pp.Combine(\n", 66 | " pp.Word(pp.nums, max=2)\n", 67 | " + \"/\"\n", 68 | " + pp.Word(pp.nums, max=2)\n", 69 | " + \"/\"\n", 70 | " + pp.Word(pp.nums, max=4)\n", 71 | ")\n", 72 | "cgr = pp.Word(pp.alphanums, exact=3)\n", 73 | "territory = pp.Word(pp.nums, min=3, max=4)\n", 74 | "zipcode = pp.Word(pp.nums, max=5)\n", 75 | "gender = pp.Word(\"MF\")\n", 76 | "ypc = pp.Combine(pp.Word(pp.nums, exact=1) + pp.Suppress(pp.Optional(\"+\")))" 77 | ] 78 | }, 79 | { 80 | "cell_type": "markdown", 81 | "metadata": {}, 82 | "source": [ 83 | "## Pre-processing\n", 84 | "First, we'll use [tika](https://brewinstall.org/Install-tika-on-Mac-with-Brew/) to convert the pdf files into xml files so we can actually parse things." 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": null, 90 | "metadata": {}, 91 | "outputs": [], 92 | "source": [ 93 | "if not os.path.exists('../data/xml'):\n", 94 | " os.makedirs('../data/xml')" 95 | ] 96 | }, 97 | { 98 | "cell_type": "markdown", 99 | "metadata": {}, 100 | "source": [ 101 | "The below command works for a `brew`-installed version of `tika` on OS X. This command may vary depending on your version of `tika`'s installation and path." 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": null, 107 | "metadata": { 108 | "scrolled": false 109 | }, 110 | "outputs": [], 111 | "source": [ 112 | "!tika -i ../data/pdf -o ../data/xml; cd ../data/xml; find . -type f -name '*.xml' -print0 | xargs -0 rename 's/\\.pdf.xml/\\.xml/'" 113 | ] 114 | }, 115 | { 116 | "cell_type": "markdown", 117 | "metadata": {}, 118 | "source": [ 119 | "## Tables" 120 | ] 121 | }, 122 | { 123 | "cell_type": "markdown", 124 | "metadata": {}, 125 | "source": [ 126 | "### CGR Definitions Table" 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "execution_count": null, 132 | "metadata": {}, 133 | "outputs": [], 134 | "source": [ 135 | "header = ['cgr', 'aa', 'bb' , 'cc', 'va', 'dd', 'hh', 'ss']\n", 136 | "description = cgr + (decimals * 7)\n", 137 | "fn = \"cgr-definitions-table\"\n", 138 | "processXML(\"../data/xml/\" + fn + \".xml\", \"../data/csv/\" + fn + \".csv\", description, header=header)" 139 | ] 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "metadata": {}, 144 | "source": [ 145 | "### CGR Premiums Table" 146 | ] 147 | }, 148 | { 149 | "cell_type": "code", 150 | "execution_count": null, 151 | "metadata": {}, 152 | "outputs": [], 153 | "source": [ 154 | "header = ['territory','gender','birthdate','ypc','current_premium','indicated_premium','selected_premium','underlying_premium','fixed_expenses','underlying_total_premium','cgr_factor','cgr']\n", 155 | "premiums = pp.Combine(\n", 156 | " pp.Word(pp.nums)\n", 157 | " + pp.ZeroOrMore(\".\" + pp.Word(pp.nums))\n", 158 | ")\n", 159 | "description = territory + gender + date + ypc + (premiums * 6) + decimals + cgr\n", 160 | "fn = \"cgr-premiums-table\"\n", 161 | "processXML(\"../data/xml/\" + fn + \".xml\", \"../data/csv/\" + fn + \".csv\", description, header=header)" 162 | ] 163 | }, 164 | { 165 | "cell_type": "markdown", 166 | "metadata": {}, 167 | "source": [ 168 | "### Territory Definitions Table" 169 | ] 170 | }, 171 | { 172 | "cell_type": "code", 173 | "execution_count": null, 174 | "metadata": {}, 175 | "outputs": [], 176 | "source": [ 177 | "header = ['county','county_code','territory','zipcode','town', 'area']\n", 178 | "numbers = pp.Word(pp.nums)\n", 179 | "text = pp.Combine(\n", 180 | " pp.Word(pp.alphas.upper()) \n", 181 | " + pp.ZeroOrMore(\" \" + pp.Word(pp.alphas.upper()))\n", 182 | " )\n", 183 | "description = text + numbers + territory + zipcode + text + numbers\n", 184 | "fn = \"territory-definitions-table\"\n", 185 | "processXML(\"../data/xml/\" + fn + \".xml\", \"../data/csv/\" + fn + \".csv\", description, header=header)" 186 | ] 187 | }, 188 | { 189 | "cell_type": "code", 190 | "execution_count": null, 191 | "metadata": {}, 192 | "outputs": [], 193 | "source": [] 194 | } 195 | ], 196 | "metadata": { 197 | "kernelspec": { 198 | "display_name": "Python 3", 199 | "language": "python", 200 | "name": "python3" 201 | }, 202 | "language_info": { 203 | "codemirror_mode": { 204 | "name": "ipython", 205 | "version": 3 206 | }, 207 | "file_extension": ".py", 208 | "mimetype": "text/x-python", 209 | "name": "python", 210 | "nbconvert_exporter": "python", 211 | "pygments_lexer": "ipython3", 212 | "version": "3.7.3" 213 | } 214 | }, 215 | "nbformat": 4, 216 | "nbformat_minor": 2 217 | } 218 | -------------------------------------------------------------------------------- /reference/cgr-premiums-table-schema.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/the-markup/investigation-allstates-algorithm/349c4f260852e9575d5892df70a2a48413471ec7/reference/cgr-premiums-table-schema.pdf -------------------------------------------------------------------------------- /reference/premium-calculation-sheet.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/the-markup/investigation-allstates-algorithm/349c4f260852e9575d5892df70a2a48413471ec7/reference/premium-calculation-sheet.pdf -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | appnope==0.1.0 2 | attrs==19.3.0 3 | backcall==0.1.0 4 | bleach==3.1.1 5 | decorator==4.4.1 6 | defusedxml==0.6.0 7 | entrypoints==0.3 8 | importlib-metadata==1.5.0 9 | ipykernel==5.1.4 10 | ipython==7.12.0 11 | ipython-genutils==0.2.0 12 | ipywidgets==7.5.1 13 | jedi==0.16.0 14 | Jinja2==2.11.1 15 | jsonschema==3.2.0 16 | jupyter==1.0.0 17 | jupyter-client==5.3.4 18 | jupyter-console==6.1.0 19 | jupyter-core==4.6.3 20 | MarkupSafe==1.1.1 21 | mistune==0.8.4 22 | nbconvert==5.6.1 23 | nbformat==5.0.4 24 | notebook==6.0.3 25 | pandocfilters==1.4.2 26 | parso==0.6.1 27 | pexpect==4.8.0 28 | pickleshare==0.7.5 29 | prometheus-client==0.7.1 30 | prompt-toolkit==3.0.3 31 | ptyprocess==0.6.0 32 | Pygments==2.5.2 33 | pyparsing==2.4.6 34 | pyrsistent==0.15.7 35 | python-dateutil==2.8.1 36 | pyzmq==18.1.1 37 | qtconsole==4.6.0 38 | Send2Trash==1.5.0 39 | six==1.14.0 40 | terminado==0.8.3 41 | testpath==0.4.4 42 | tornado==6.0.3 43 | traitlets==4.3.3 44 | wcwidth==0.1.8 45 | webencodings==0.5.1 46 | widgetsnbextension==3.5.1 47 | zipp==3.0.0 48 | -------------------------------------------------------------------------------- /rmd/analysis.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "How We Analyzed Allstate’s Car Insurance Algorithm" 3 | author: "Maddy Varner, Dina Haner, Andrew Cohen and Aaron Sankin" 4 | output: html_notebook 5 | --- 6 | 7 | # How We Analyzed Allstate’s Car Insurance Algorithm 8 | _By Maddy Varner, Dina Haner, Andrew Cohen and Aaron Sankin_ 9 | 10 | ## Code Setup 11 | 12 | ### Merging 13 | ```{r setup, include=FALSE} 14 | ##Define package list 15 | Packages<-c("readr","ggpubr","ggplot2","reshape2","rpart","caret","ggforce","tidyverse","lubridate","gridExtra", 16 | "scales","choroplethr","gridExtra","broom","kableExtra","tidyr","dplyr") 17 | .packages = Packages 18 | 19 | ##Install CRAN packages (if not already installed) 20 | .inst <- .packages %in% installed.packages() 21 | if(length(.packages[!.inst]) > 0) install.packages(.packages[!.inst]) 22 | 23 | ##Load packages into session 24 | lapply(.packages, require, character.only=TRUE) 25 | ``` 26 | 27 | First, we want a list of filenames. You should make sure the paths given here are correct for you! 28 | ```{r echo=FALSE} 29 | # PUT FILENAMES HERE 30 | data_folder = '../data/csv/' 31 | 32 | fn_dp03 <- paste(data_folder,"ACS_MD_15_5YR_DP03.csv", sep="") 33 | fn_dp05 <- paste(data_folder,"ACS_MD_15_5YR_DP05.csv", sep="") 34 | 35 | fn_customers <- paste(data_folder, "cgr-premiums-table.csv", sep="") 36 | fn_territories <- paste(data_folder, "territory-definitions-table.csv", sep="") 37 | fn_cgr <- paste(data_folder, "cgr-definitions-table.csv", sep="") 38 | ``` 39 | 40 | Then, we'll load the US Census tables -- We're using ZCTA-level data from the 2014 5-Year American Community Survey. 41 | ```{r echo=FALSE} 42 | acs_dp03 <- read_csv(fn_dp03) 43 | acs_dp05 <- read_csv(fn_dp05) 44 | 45 | acs_dp05$population <- acs_dp05$HC01_VC94 + acs_dp05$HC01_VC88 + acs_dp05$HC01_VC95 + acs_dp05$HC01_VC96 + acs_dp05$HC01_VC97 + acs_dp05$HC01_VC98 + acs_dp05$HC01_VC99 46 | acs_dp05$percentage_nonwhite <- 1.0 * (acs_dp05$population - acs_dp05$HC01_VC94) / acs_dp05$population 47 | acs_dp05$majority_minority <- acs_dp05$percentage_nonwhite > 0.5 48 | race <- acs_dp05[,c('GEO.id2', 'population', 'percentage_nonwhite', 'majority_minority')] 49 | 50 | acs_dp03$median_income <- acs_dp03$HC01_VC85 51 | income <- acs_dp03[,c('GEO.id2', "median_income")] 52 | ``` 53 | 54 | Then we will load tables from the Allstate filing. 55 | ```{r echo=FALSE} 56 | maryland <- read_csv(fn_customers) 57 | territory <- read_csv(fn_territories) 58 | cgr <- read_csv(fn_cgr) 59 | 60 | initial_row_count <- nrow(maryland) 61 | ``` 62 | 63 | Next, we'll merge tables together. 64 | ```{r echo=FALSE} 65 | ## The CGR factor included rounds to the hundredth place. Use the factor included in the CGR definition table. 66 | cgr <- cgr[,c('cgr', "aa")] 67 | maryland <- merge(maryland, cgr, by="cgr") 68 | maryland$cgr_factor <- maryland$aa 69 | maryland$aa <- NULL 70 | 71 | ## Drop duplicate zip codes in the territory table 72 | territory <- territory[order(territory$territory, decreasing=TRUE),] 73 | territory <- territory[!duplicated(territory$territory),] 74 | 75 | ## Let's merge Allstate's "territory definitions" (geographic data) with each row so we can use Census data 76 | maryland <- merge(maryland, territory, by="territory") 77 | 78 | ## Finally, let's merge the ACS data for each Maryland policy holder 79 | maryland <- merge(maryland, race, by.x = "zipcode", by.y="GEO.id2") 80 | maryland <- merge(maryland, income, by.x = "zipcode", by.y="GEO.id2") 81 | ``` 82 | 83 | 84 | ### Feature Engineering 85 | #### Normalizing and Bucketing 86 | Now we will perform feature engineering by normalize values, and create buckets based on the age of the policy holder. 87 | ```{r echo=FALSE} 88 | ## Let's rename the variables from the Maryland table so they make sense! 89 | maryland$current_price <- maryland$current_premium 90 | maryland$transition_price <- maryland$selected_premium 91 | maryland$ideal_price <- maryland$indicated_premium 92 | 93 | ## Let's get the percentage change of indicated and selected premiums. 94 | maryland$price_change <- (maryland$transition_price - maryland$current_price) / maryland$current_price * 100 95 | maryland$ideal_price_change <- (maryland$ideal_price - maryland$current_price) / maryland$current_price * 100 96 | 97 | ## We also want to calculate the age of each policy holder using: 98 | ### The filing's requested effective date, 04/11/2014 99 | ### The policy holder's birthdate 100 | effective_date <- as.Date("04/11/2014", "%m/%d/%Y") 101 | maryland$age <- floor(time_length(difftime(effective_date, as.Date(maryland$birthdate, "%m/%d/%Y")),"years")) 102 | 103 | ##categorize into three buckets of age as observed in dataset 104 | maryland$age_bucket<-ifelse( 105 | maryland$age<41, 106 | "16-40", 107 | ifelse(maryland$age>40&maryland$age<63, 108 | "41-62","63+" 109 | ) 110 | ) 111 | 112 | ## We will break ages the rough categories of percentage shifts as observed in the "Effects of the Retention Model on Maryland Customers" section of the white paper 113 | maryland$shift_category <- ifelse((maryland$price_change >= 9.5),"Large Increase","Small Discount") 114 | maryland$shift_category <- ifelse((maryland$current_price < maryland$transition_price) & maryland$shift_category == "Small Discount","Small Increase",maryland$shift_category) 115 | 116 | ## We will take the binomial signals identified in the paper and turn them ino booleans 117 | 118 | maryland$is_above_current <- maryland$current_price >= 1883 119 | maryland$is_above_difference <- maryland$ideal_price - maryland$current_price >= 381 120 | 121 | maryland$is_both <- maryland$is_above_current & maryland$is_above_difference 122 | 123 | maryland$IndMinCur <- maryland$indicated_premium - maryland$current_premium 124 | maryland$SelMinCur <- maryland$selected_premium - maryland$current_premium 125 | maryland$SelDivCur <- maryland$selected_premium / maryland$current_premium 126 | maryland$IncDivCur <- maryland$indicated_premium / maryland$current_premium 127 | maryland$SelDivInd <- maryland$selected_premium / maryland$indicated_premium 128 | 129 | ##categorize into three buckets 130 | maryland$IncCat<-ifelse( 131 | maryland$SelDivCur<=1, 132 | "Decreased or No Change", 133 | ifelse(maryland$SelDivCur>1&maryland$SelDivCur<1.0502, 134 | "Small Increase","Large Increase" 135 | ) 136 | ) 137 | 138 | ##cut percentange_nonwhite into four equal intervals 139 | maryland$PNW_bucket4<-cut(maryland$percentage_nonwhite, 140 | breaks=seq(0,1,0.25), 141 | labels=c("0-0.25",">0.25-.5",">0.5-0.75",">0.75-1")) 142 | ``` 143 | 144 | #### Filtering out rows 145 | We will now filter out policy holders without demographic data. 146 | ```{r} 147 | md <- filter(maryland, !is.na(median_income) & !is.na(percentage_nonwhite)) 148 | ``` 149 | 150 | #### How many policy holders were removed? 151 | ```{r} 152 | clean_row_count <- nrow(md) 153 | paste("Initial rows:", initial_row_count) 154 | paste("Rows used in analysis:",clean_row_count) 155 | paste("Rows removed:",initial_row_count - clean_row_count) 156 | ``` 157 | 158 | 159 | ## Abstract 160 | 161 | ```{r} 162 | ## In Allstate’s filing, it indicated more than half of its customers... 163 | nrow(filter(md, current_premium > indicated_premium)) / nrow(md) 164 | ``` 165 | 166 | ```{r} 167 | # Divided increases into 10 groups 168 | quantile(filter(md, indicated_premium > current_premium)$SelMinCur, prob = seq(0, 1, length = 11)) # > 78.560 169 | quantile(filter(md, indicated_premium > current_premium)$SelDivCur, prob = seq(0, 1, length = 11)) # > 1.049705 170 | 171 | median(md$current_premium) 172 | median(filter(md, SelMinCur > 78.560)$current_premium) 173 | median(filter(md, SelDivCur > 1.049705)$current_premium) 174 | 175 | ``` 176 | 177 | 178 | ```{r echo= FALSE} 179 | #max(filter(md, IncCat == "Small Increase")$SelDivCur) 180 | ``` 181 | 182 | 183 | ```{r echo=FALSE} 184 | 185 | ## '...this age group had the highest median current prices' 186 | young <- md %>% filter(age < 41) 187 | middle <- md %>% filter(41 <= age & age <= 62) 188 | old <- md %>% filter(62 < age) 189 | 190 | median(young$current_premium) 191 | median(middle$current_premium) 192 | median(old$current_premium) 193 | ``` 194 | 195 | ```{r echo=FALSE} 196 | ## while Allstate’s own data stated that more than half of its customers in Maryland were being overcharged, 197 | paste0(nrow(filter(md, indicated_premium < current_premium)) / nrow(md) * 100, "%") 198 | 199 | ## that none of them should be given a substantial discount. (largest discount is 32.80, "ideal" discount was $868.30 for that person) 200 | head(select(md[order(md$SelMinCur),], current_premium, indicated_premium, selected_premium, IndMinCur, SelMinCur), 1) 201 | 202 | ## Customers aged 63 and older were most affected by the lack of meaningful discounts. 203 | md %>% group_by(IncCat, age_bucket) %>% summarise(n = n(), median_ideal = median(IndMinCur), mean_ideal = mean(IndMinCur)) 204 | ``` 205 | 206 | ## Introduction 207 | 208 | ## Methodology 209 | 210 | ### How We Acquired the Data 211 | 212 | 213 | ```{r echo=FALSE} 214 | paste0('with individual pricing information for ', initial_row_count, ' Allstate policyholders') 215 | ``` 216 | 217 | ```{r echo=FALSE} 218 | paste0('Of the ', initial_row_count, ' entries, ', (initial_row_count - clean_row_count),' had zip codes not included in the ACS data, and were removed.') 219 | ``` 220 | 221 | ### How Allstate’s Proposed Pricing in Maryland Worked 222 | 223 | ```{r echo=FALSE} 224 | nrow(filter(md, IndMinCur > 2000)) 225 | nrow(filter(md, IndMinCur > 200)) 226 | ``` 227 | 228 | ```{r} 229 | ## If we control for indicated minus current? 230 | #varImp(fit) 231 | ``` 232 | 233 | ```{r echo=FALSE} 234 | md %>% filter(age == 32) %>% filter(gender == "M") %>% filter(county == "PRINCE GEORGES") %>% filter(current_premium == 2617.42) %>% select(age, gender, county, current_premium, IncDivCur, SelDivCur, SelMinCur, selected_premium) 235 | ``` 236 | 237 | ```{r echo=FALSE} 238 | md %>% filter(age == 32) %>% filter(gender == "M") %>% filter(county == "PRINCE GEORGES") %>% filter(current_premium == 814.48) %>% select(age, gender, county, current_premium, IncDivCur, SelDivCur, SelMinCur, selected_premium) 239 | ``` 240 | 241 | ### Effects of the Proposed Retention Model on Maryland Customers 242 | 243 | ```{r Linear Models,include=TRUE} 244 | 245 | ##Main Effects model 246 | lm0<-lm(selected_premium~current_premium+indicated_premium,data=md)#Fit the model 247 | summ_lm0<-tidy(lm0)#Get results in nice table 248 | summ_lm0$AdjRSqu<-c(round(summary(lm0)$adj.r.squared,4),"-","-")#Pull R-square estimate from model summary 249 | summ_lm0$estimate<-round(summ_lm0$estimate,2) 250 | summ_lm0$std.error<-round(summ_lm0$std.error,4) 251 | summ_lm0$statistic<-round(summ_lm0$statistic,2) 252 | summ_lm0$p.value<-round(summ_lm0$p.value,4) 253 | AddRow<-t(data.frame(c("current_premium*indicated_premium","-","-","-","-","-")))#Add additional row for easy binding with full interaction model table 254 | colnames(AddRow)<-colnames(summ_lm0) 255 | summ_lm0<-rbind(summ_lm0,AddRow) 256 | rownames(summ_lm0) <- NULL#Remove rownames 257 | 258 | ##Full Interaction model 259 | lm1<-lm(selected_premium~current_premium*indicated_premium,data=md) 260 | summ_lm1<-tidy(lm1) 261 | summ_lm1$AdjRSqu<-c(round(summary(lm1)$adj.r.squared,4),"-","-","-") 262 | summ_lm1$estimate<-round(summ_lm1$estimate,2) 263 | summ_lm1$std.error<-round(summ_lm1$std.error,4) 264 | summ_lm1$statistic<-round(summ_lm1$statistic,2) 265 | summ_lm1$p.value<-round(summ_lm1$p.value,4) 266 | summ_lm1<-summ_lm1%>% 267 | select("estimate", "std.error", "statistic", 268 | "p.value", "AdjRSqu") 269 | 270 | summ_lm<-cbind(summ_lm0,summ_lm1)#Put the two tables together 271 | 272 | ##Outputs nice table in markdown file 273 | kable(summ_lm) %>% 274 | kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))%>% 275 | add_header_above(c(" " = 1, 276 | "Main Effects" = 5, "Full Interaction" = 5))%>% 277 | add_header_above(c("Model Output for Both Main Effects and Full Interaction Models Using Current and Indicated as Predictors"=11)) 278 | 279 | 280 | ##Main Effects model using IndMinCur instead of indicated 281 | lm2<-lm(selected_premium~current_premium+IndMinCur,data=md)#Fit the model 282 | summ_lm2<-tidy(lm2)#Get results in nice table 283 | summ_lm2$AdjRSqu<-c(round(summary(lm2)$adj.r.squared,4),"-","-")#Pull R-square estimate from model summary 284 | summ_lm2$estimate<-round(summ_lm2$estimate,2) 285 | summ_lm2$std.error<-round(summ_lm2$std.error,4) 286 | summ_lm2$statistic<-round(summ_lm2$statistic,2) 287 | summ_lm2$p.value<-round(summ_lm2$p.value,4) 288 | AddRow<-t(data.frame(c("current_premium*IndMinCur","-","-","-","-","-")))#Add additional row for easy binding with full interaction model table 289 | colnames(AddRow)<-colnames(summ_lm2) 290 | summ_lm2<-rbind(summ_lm2,AddRow) 291 | rownames(summ_lm2) <- NULL#Remove rownames 292 | 293 | ##Full Interaction model using IndMinCur instead of indicated 294 | lm3<-lm(selected_premium~current_premium*IndMinCur,data=md) 295 | summ_lm3<-tidy(lm3) 296 | summ_lm3$AdjRSqu<-c(round(summary(lm3)$adj.r.squared,4),"-","-","-") 297 | summ_lm3$estimate<-round(summ_lm3$estimate,2) 298 | summ_lm3$std.error<-round(summ_lm3$std.error,4) 299 | summ_lm3$statistic<-round(summ_lm3$statistic,2) 300 | summ_lm3$p.value<-round(summ_lm3$p.value,4) 301 | summ_lm3<-summ_lm3%>% 302 | select("estimate", "std.error", "statistic", 303 | "p.value", "AdjRSqu") 304 | 305 | summ_lm_IndMinCur<-cbind(summ_lm2,summ_lm3)#Put the two tables together 306 | 307 | kable(summ_lm_IndMinCur) %>% 308 | kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))%>% 309 | add_header_above(c(" " = 1, 310 | "Main Effects" = 5, "Full Interaction" = 5))%>% 311 | add_header_above(c("Model Output for Both Main Effects and Full Interaction Models Using Current and (Indicated-Current) as Predictors"=11)) 312 | 313 | ``` 314 | 315 | ```{r Residuals Charts, include=TRUE} 316 | 317 | ##Extract data and define variables for plotting 318 | ResModelSet<-cbind(lm0[["model"]],lm0$residuals) 319 | colnames(ResModelSet)[4]<-"Residuals" 320 | ResModelSet$PriceChange<-(ResModelSet$selected_premium/ResModelSet$current_premium-1)*100 321 | ResModelSet$IndMinCur<-ResModelSet$indicated_premium-ResModelSet$current_premium 322 | ResModelSet$Index <- 1:nrow(ResModelSet) 323 | ResModelSet$IncCat<-ifelse( 324 | ResModelSet$PriceChange<=0, 325 | "Decreased or No Change", 326 | ifelse(ResModelSet$PriceChange>0&ResModelSet$PriceChange<5.02, 327 | "Small Increase","Large Increase" 328 | ) 329 | ) 330 | 331 | ##Plot Residuls across index colored by Price Change 332 | residuals.chart <- ggplot(ResModelSet,aes(x=Index,y=Residuals,col=PriceChange))+ 333 | geom_point()+ 334 | scale_color_gradient2(midpoint=7, low="blue", mid="orange", 335 | high="red", space ="Lab" , 336 | name = "Price\nChange (%)")+ 337 | ggtitle("\n\nRegression Residuals vs Index", 338 | subtitle="We expect to see a normally distributed scattering around Residuals=0")+ 339 | theme_minimal(base_size=20,base_line_size=11/22 340 | ) 341 | 342 | ##Turn IncCat to factor so manual ordering can be done 343 | ResModelSet<-transform(ResModelSet, 344 | IncCat=factor(IncCat, 345 | levels=c("Decreased or No Change", 346 | "Small Increase", 347 | "Large Increase"))) 348 | 349 | 350 | ##Plot Price Change vs Residuals clored by Price Change 351 | change.v.residuals.chart <- ggplot(ResModelSet,aes(x=Residuals,y=PriceChange,col=PriceChange))+ 352 | geom_point()+xlab("Residuals ($)")+ 353 | geom_hline(yintercept=9)+ geom_hline(yintercept=0)+ 354 | scale_y_continuous(name = "Price Change (%)", 355 | #labels = percent, 356 | limits = c(-2.5,22.5), 357 | breaks=seq(-2.5,22.5,2.5))+ #ylim(-5,25)+ 358 | ggtitle("\n\nPrice Change vs Regression Residuals", 359 | subtitle="Observable anamolous shift in the data")+ 360 | scale_color_gradient2(midpoint=7, low="blue", mid="orange", 361 | high="red", space ="Lab", 362 | name = "Price\nChange")+ 363 | annotate("text", label = "Policies that received an increase >= 9.5%", 364 | x =-180 , y = 15, size = 5, colour = "black")+ 365 | annotate("text", label = "Policies that received an increase < 9.5%", 366 | x =500 , y = 5, size = 5, colour = "black")+ 367 | annotate("text", label = "Policies that received a decrease", 368 | x =500 , y = -1, size = 5, colour = "black")+ 369 | theme_minimal(base_size=20,base_line_size=11/22 370 | ) 371 | 372 | ##Zoom in on histograms of each Increase Category 373 | change.distributions.chart <- ggplot(ResModelSet,aes(x=PriceChange))+ 374 | geom_histogram(aes(fill=IncCat),bins = 200)+ 375 | facet_wrap(~IncCat,scales = "free")+ 376 | #ylim(0,4500)+ 377 | scale_fill_manual(values=c("darkviolet","orange","Red"), 378 | name="Increase Category")+ 379 | ggtitle("\n\nPrice Change Distributions by Increase Category", 380 | subtitle="Increase categories are PC<=0, 0=9.5")+ 381 | theme_minimal(base_size=20,base_line_size=11/22 382 | )+ylab("Frequency (bins=200)")+xlab("Price Change (%)")+ 383 | theme(axis.text.x = element_text(angle = 70))+ 384 | theme(legend.position = "none") 385 | 386 | ##Table with counts and corresponding proportions of Increase Categories 387 | table1<-aggregate(ResModelSet$PriceChange, 388 | by=list(ResModelSet$IncCat), 389 | FUN="length") 390 | colnames(table1)<-c("Increase Category","Count") 391 | table1$Proportion<-round(table1$Count/sum(table1$Count),4) 392 | 393 | ##Display table with counts 394 | kable(table1) %>% 395 | kable_styling(full_width = F)%>% 396 | add_header_above(c("Proportion of Sample by Increase Category"=3)) 397 | 398 | change.distributions.chart 399 | change.v.residuals.chart 400 | residuals.chart 401 | ``` 402 | 403 | ```{r Large Increase Payers Already Pay A Lot, echo=FALSE} 404 | ## $1883 -- the minimum current premium found in the increase group, is in the top 10% of all current premiums. 405 | quantile(md$current_premium, prob = seq(0, 1, length = 11)) 406 | min(filter(md, SelDivCur > 1.095)$current_premium) 407 | ``` 408 | 409 | ```{r Scatter Plot, include = TRUE} 410 | ##Final premium vs Current Premium 411 | final.v.current.chart <- ggplot(md,aes(x=current_premium,y=selected_premium,col=price_change))+ 412 | geom_point()+ 413 | scale_y_continuous(name = "Selected Premium", 414 | labels = dollar, 415 | limits = c(0,9000), 416 | breaks=seq(0,9000,300))+ #ylim(-5,25)+ 417 | scale_x_continuous(name = "Current Premium", 418 | labels = dollar, 419 | limits = c(0,9000), 420 | breaks=seq(0,9000,300))+ #ylim(-5,25)+ 421 | ggtitle("\n\nSelected Premium vs Current Premium", 422 | subtitle="Observable anamolous shift in the data")+ 423 | geom_vline(xintercept=1883)+ 424 | scale_color_gradient2(midpoint=7, low="blue", mid="orange", 425 | high="red", space ="Lab", 426 | name = "Price\nChange")+ 427 | annotate("text", label = "All policies that received an increase >= 9.5% had a current premium >=$1883", 428 | x =4800 , y = 9000, size = 4, colour = "black")+ 429 | theme_minimal(base_size=20,base_line_size=11/22)+ 430 | theme(axis.text.x = element_text(angle = 70)) 431 | 432 | ##Final Premium vs Indicated Premium 433 | final.v.indicated.chart <- ggplot(md,aes(x=indicated_premium,y=selected_premium,col=price_change))+ 434 | geom_point()+ 435 | scale_y_continuous(name = "Selected Premium", 436 | labels = dollar, 437 | limits = c(0,9000), 438 | breaks=seq(0,9000,300))+ #ylim(-5,25)+ 439 | scale_x_continuous(name = "Indicated Premium", 440 | labels = dollar, 441 | limits = c(0,9000), 442 | breaks=seq(0,9000,300))+ #ylim(-5,25)+ 443 | ggtitle("\n\nSelected Premium vs Indicated Premium", 444 | subtitle="Observable anamolous shift in the data")+ 445 | scale_color_gradient2(midpoint=7, low="blue", mid="orange", 446 | high="red", space ="Lab", 447 | name = "Price\nChange")+ 448 | # annotate("text", label = "What to say here", 449 | # x =5000 , y = 9000, size = 4, colour = "black")+ 450 | theme_minimal(base_size=20,base_line_size=11/22)+ 451 | theme(axis.text.x = element_text(angle = 70)) 452 | 453 | ##Indicated minus Current vs Current Premium 454 | difference.v.current.chart <- ggplot(md,aes(y=(indicated_premium-current_premium),x=current_premium,col=price_change))+ 455 | geom_point()+ 456 | scale_y_continuous(name = "Indicated Premium minus Current Premium", 457 | labels = dollar, 458 | limits = c(-5000,8000), 459 | breaks=seq(-8000,8000,500))+ 460 | scale_x_continuous(name = "Current Premium", 461 | labels = dollar, 462 | limits = c(0,9000), 463 | breaks=seq(0,9000,300))+ 464 | ggtitle("\n\nIndicated Minus Current vs Current Premium", 465 | subtitle="Observable boundaries of shifted group")+ 466 | scale_color_gradient2(midpoint=7, low="blue", mid="orange", 467 | high="red", space ="Lab", 468 | name = "Price\nChange")+ 469 | annotate("text", label = ("All policies that received an increase >= 9.5% had a current premium >=$1883"), 470 | x =5550 , y = 8000, size = 4, colour = "black")+ 471 | annotate("text", label = ("All policies that received an increase >= 9.5% had a difference between current and indicated >=$381"), 472 | x =5550 , y = 7500, size = 4, colour = "black")+ 473 | theme_minimal(base_size=20,base_line_size=11/22)+ 474 | theme(axis.text.x = element_text(angle = 70)) 475 | 476 | ##Proportion tables and testing next section 477 | final.v.current.chart 478 | final.v.indicated.chart 479 | difference.v.current.chart 480 | ``` 481 | 482 | ```{r echo=FALSE} 483 | ## N% of Maryland customers would have recieved... 484 | md %>% filter(IncCat == "Decreased or No Change") %>% nrow() / nrow(md) * 100 485 | 486 | paste0("No customer got a transition discount greater than ", (1 - min(md$SelDivCur)) * 100, "%") 487 | 488 | ## These customers had a median difference 489 | discounts <- md %>% filter(IncCat == "Decreased or No Change") 490 | median(discounts$IndMinCur) 491 | median(discounts$SelMinCur) 492 | 493 | ## More than half of all Maryland customers were owed 494 | md %>% filter(indicated_premium < current_premium) %>% nrow() / nrow(md) * 100 495 | 496 | ## Who got no discounts? 497 | md %>% filter(current_premium == selected_premium) %>% nrow() 498 | 499 | ## Who got no discounts and had the largest discount owed? 500 | md %>% filter(current_premium == selected_premium) %>% select(current_premium, indicated_premium, selected_premium, IndMinCur) %>% arrange((IndMinCur)) 501 | ``` 502 | 503 | ```{r echo=FALSE} 504 | md %>% filter(current_premium == 6149.75) %>% filter(indicated_premium == 1795.52) %>% select(gender, current_premium, indicated_premium, selected_premium, SelMinCur) 505 | ``` 506 | 507 | ```{r echo=FALSE} 508 | small <- md %>% filter(IncCat == "Small Increase") 509 | 510 | paste0((nrow(small) / nrow(md) * 100), '% of Maryland customers would have received a small transition increase') 511 | 512 | ## In this category, 513 | median(small$IncDivCur) 514 | 515 | ## Instead of such a large increase 516 | max(small$SelDivCur) 517 | ``` 518 | 519 | ```{r echo=FALSE} 520 | large <- md %>% filter(IncCat == "Large Increase") 521 | 522 | paste0(nrow(large) / nrow(md) * 100, "% of Maryland customers would have received a much larger transition increase.") 523 | 524 | paste0("These increases ranged from ", (min(large$SelDivCur) - 1) * 100, " to ", (max(large$SelDivCur) - 1) * 100," percent.") 525 | 526 | ## These customers were already paying some of the highest premiums. 527 | quantile(md$current_premium, prob = seq(0, 1, length = 11)) 528 | min(filter(md, SelDivCur > 1.095)$current_premium) 529 | 530 | paste0("The smallest current premium being paid by customers in this group was $", min(large$current_premium)) 531 | 532 | ## More than double 533 | min(large$current_premium) / median(md$current_premium) 534 | 535 | paste0("the median overall price customers in the dataset were paying at the time of the proposal, $", median(md$current_premium)) 536 | ``` 537 | 538 | 539 | 540 | ### Exploring the Retention Model Effects with CART Analysis 541 | 542 | ```{r CART Analysis, include=TRUE} 543 | set.seed(216) 544 | inTraining <- createDataPartition(md$IncCat, p = .8, list = FALSE) 545 | training <- md[ inTraining, ] 546 | testing <- md[ -inTraining, ] 547 | 548 | fitControl <- trainControl(## 5-fold CV 549 | method = "repeatedcv", 550 | number = 5, 551 | ## repeated ten times 552 | repeats = 5) 553 | 554 | set.seed(614) 555 | fit <- train(IncCat ~ current_premium + IndMinCur + gender + age + ypc + median_income + majority_minority, 556 | data = training, 557 | method="rpart2", 558 | trControl = fitControl 559 | ) 560 | ``` 561 | 562 | ```{r} 563 | pred <- predict(fit, testing) 564 | confusionMatrix(pred, as.factor(testing$IncCat)) 565 | 566 | length(pred) 567 | ``` 568 | 569 | ```{r Decision Tree Visual} 570 | plot(fit$finalModel) 571 | text(fit$finalModel) 572 | ``` 573 | 574 | ```{r} 575 | varImp(fit) 576 | ``` 577 | 578 | ### Disproportionate Effects 579 | 580 | ```{r Check Props Function,include=FALSE} 581 | ##function to get proportions tables 582 | CheckPropFun<-function(CatVarName,df){ 583 | 584 | df1<-df 585 | CheckProp<-aggregate(df1$SelDivCur, 586 | by=list(df1$IncCat,df1[[CatVarName]]),FUN="length") 587 | TotalCatVar<-aggregate(CheckProp$x,by=list(CheckProp$Group.2),FUN="sum") 588 | TotalCatVar$Totals<-round(sum(TotalCatVar$x),4) 589 | TotalCatVar$Proportions<-round(TotalCatVar$x/sum(TotalCatVar$x),4) 590 | colnames(TotalCatVar)[1]<-"Group.2" 591 | TotalCatVar$Group.1<-"Group Total" 592 | TotalCatVar<-TotalCatVar[c(5,1,2,3,4)] 593 | IncCatTotals<-aggregate(df1$SelDivCur, 594 | by=list(df1$IncCat),FUN="length") 595 | colnames(IncCatTotals)[2]<-"Totals" 596 | CheckProp<-merge(CheckProp,IncCatTotals) 597 | # CheckProp$Totals<-ifelse(CheckProp$Group.1=="Decreased or No Change", 598 | # IncCatTotals$x[1], 599 | # ifelse(CheckProp$Group.1=="Large Increase", 600 | # IncCatTotals$x[2], 601 | # IncCatTotals$x[3]) 602 | # ) 603 | 604 | CheckProp$Proportions<-round(CheckProp$x/CheckProp$Totals,10) 605 | names_cat<-unique(CheckProp$Group.2) 606 | lengthnames<-length(names_cat) 607 | 608 | ##Test each proportion against pop proprotion 609 | ##################################################################### 610 | for(k in 1:lengthnames){ 611 | 612 | Subset<-CheckProp%>% 613 | filter(Group.2==names_cat[k]) 614 | succ1vec<-c(Subset$x[1],TotalCatVar$x[which(TotalCatVar$Group.2==Subset$Group.2[1])]) 615 | succ2vec<-c(Subset$x[3],TotalCatVar$x[which(TotalCatVar$Group.2==Subset$Group.2[1])]) 616 | succ3vec<-c(Subset$x[2],TotalCatVar$x[which(TotalCatVar$Group.2==Subset$Group.2[1])]) 617 | tot1vec<-c(Subset$Totals[1],TotalCatVar$Totals[which(TotalCatVar$Group.2==Subset$Group.2[1])]) 618 | tot2vec<-c(Subset$Totals[3],TotalCatVar$Totals[which(TotalCatVar$Group.2==Subset$Group.2[1])]) 619 | tot3vec<-c(Subset$Totals[2],TotalCatVar$Totals[which(TotalCatVar$Group.2==Subset$Group.2[1])]) 620 | test1<-prop.test(succ1vec,tot1vec,p=NULL,alternative = "two.sided",conf.level = 0.9999)$p.value 621 | test2<-prop.test(succ2vec,tot2vec,p=NULL,alternative = "two.sided",conf.level = 0.9999)$p.value 622 | test3<-prop.test(succ3vec,tot3vec,p=NULL,alternative = "two.sided",conf.level = 0.9999)$p.value 623 | 624 | if(test1<=0.0001|test2<=0.0001|test3<=0.0001){ 625 | dec0<-"Yes" 626 | }else{dec0<-"No"} 627 | 628 | if(k == 1){ 629 | dec2<-dec0 630 | }else{dec2<-c(dec2,dec0)} 631 | } 632 | dec3<-data.frame(dec2,names_cat) 633 | 634 | ##################################################################### 635 | 636 | ##Test multiple proportions against each other 637 | ##################################################################### 638 | 639 | for(k in 1:lengthnames){ 640 | 641 | propvec<-CheckProp%>% 642 | filter(Group.2==names_cat[k])%>% 643 | dplyr::select(Proportions) 644 | samplesize = CheckProp%>% 645 | filter(Group.2==names_cat[k])%>% 646 | dplyr::select(x) 647 | 648 | p = propvec$Proportions 649 | N = length(p) 650 | ss = samplesize$x 651 | value = critical.range = c() 652 | 653 | ## Compute critical values. 654 | for (i in 1:(N-1)){ 655 | for (j in (i+1):N){ 656 | value = round(c(value,(abs(p[i]-p[j]))),10) 657 | critical.range = c(critical.range, 658 | sqrt(qchisq(.9999,4))*sqrt(p[i]*(1-p[i])/ss[i] + p[j]*(1-p[j])/ss[j])) 659 | }} 660 | 661 | prop_test<-as.data.frame(round(cbind(value,critical.range),8)) 662 | prop_test$SigDec<-ifelse(prop_test$value>=prop_test$critical.range,"Yes","No") 663 | colnames(prop_test)<-c("value","critical.range","SigDec") 664 | if("Yes" %in% prop_test$SigDec){ 665 | dec0<-"Yes" 666 | }else{dec0<-"No"} 667 | 668 | if(k == 1){ 669 | dec<-dec0 670 | }else{dec<-c(dec,dec0)} 671 | 672 | #return(dec) 673 | } 674 | dec4<-data.frame(dec,names_cat) 675 | ##################################################################### 676 | 677 | CheckProp0<-CheckProp%>% 678 | dplyr::select(Group.2,Group.1,Proportions) 679 | CheckProp0$Proportions<-round(CheckProp0$Proportions,4) 680 | CheckProp1<-data.frame(spread(CheckProp0, Group.1, Proportions),TotalCatVar$Proportions) 681 | #CheckProp2<-cbind(TotalCatVar$Proportions,CheckProp1) 682 | colnames(CheckProp1)[1]<-CatVarName 683 | CheckProp2<-CheckProp1[c(1,5,2,4,3)] 684 | colnames(CheckProp2)[2]<-"Group Total" 685 | colnames(CheckProp2)[3]<-"Decreased or No Change" 686 | colnames(CheckProp2)[4]<-"Small Increase" 687 | colnames(CheckProp2)[5]<-"Large Increase" 688 | colnames(dec3)[2]<-CatVarName 689 | colnames(dec4)[2]<-CatVarName 690 | CheckProp3<-merge(CheckProp2,dec3) 691 | CheckProp4<-merge(CheckProp3,dec4) 692 | colnames(CheckProp4)[6]<-"Diff1" 693 | colnames(CheckProp4)[7]<-"Diff2" 694 | CheckProp4 695 | } 696 | 697 | 698 | ``` 699 | 700 | ####Gender of Oldest Operator 701 | 702 | ```{r Checking Gender, include=TRUE} 703 | 704 | # ggplot(md,aes(x=gender))+ 705 | # geom_bar() 706 | 707 | FP_gender<-CheckPropFun("gender",md) 708 | kable(FP_gender,align = "c") %>% 709 | kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))%>% 710 | add_header_above(c("Variable" = 1, 711 | "Population Proportion" = 1, 712 | "Increase Category" = 3, 713 | "Significant Diffs"=2))%>% 714 | add_header_above(c("\n\nProportion Table for gender"=7)) 715 | 716 | FP_gender_long1<-gather(FP_gender,"Increase Category","Proportion",`Group Total`:`Large Increase`) 717 | FP_gender_long1<-transform(FP_gender_long1, 718 | IncCat=factor(`Increase Category`, 719 | levels=c("Group Total", 720 | "Decreased or No Change", 721 | "Small Increase", 722 | "Large Increase"))) 723 | 724 | gender.chart <- ggplot(FP_gender_long1,aes(x=gender,y=Proportion,fill=IncCat))+ 725 | geom_bar(aes(y=Proportion),stat="identity",position = "dodge")+ 726 | scale_fill_manual(values=c("grey","darkviolet","orange","Red"),name="Increase Category")+ 727 | geom_text(aes(label=paste0(Proportion*100, "%")),position = position_dodge(width=1), angle=45)+ 728 | scale_y_continuous(labels=scales::percent)+ 729 | xlab("Gender")+ 730 | ggtitle("\n\nIncrease Category Proportion by Gender", 731 | subtitle="The Group Total is the sample proportion")+ 732 | theme_minimal(base_size=20,base_line_size=11/22) 733 | 734 | gender.chart 735 | ``` 736 | 737 | 738 | ```{r, echo=FALSE} 739 | ## Men 25 or younger with large increases: 740 | md %>% filter(gender=="M") %>% filter(age <= 25) %>% filter(IncCat == "Large Increase") %>% nrow() 741 | 742 | ## Men with large increases: 743 | md %>% filter(gender == "M") %>% filter(IncCat == "Large Increase") %>% nrow() 744 | ``` 745 | 746 | ```{r Gender 25 and Under Chart} 747 | 748 | FP_genderlt25_df<-md%>% 749 | filter(age<=25) 750 | FP_genderlt25<-CheckPropFun("gender",FP_genderlt25_df) 751 | kable(FP_genderlt25,align = "c") %>% 752 | kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))%>% 753 | add_header_above(c("Variable" = 1, 754 | "Population Proportion" = 1, 755 | "Increase Category" = 3, 756 | "Significant Diffs"=2))%>% 757 | add_header_above(c("\n\nProportion Table for gender, age<=25"=7)) 758 | 759 | FP_gender_longlt25<-gather(FP_genderlt25,"Increase Category","Proportion",`Group Total`:`Large Increase`) 760 | FP_gender_longlt25<-transform(FP_gender_longlt25, 761 | IncCat=factor(`Increase Category`, 762 | levels=c("Group Total", 763 | "Decreased or No Change", 764 | "Small Increase", 765 | "Large Increase"))) 766 | 767 | gender.25under.chart <- ggplot(FP_gender_longlt25,aes(x=gender,y=Proportion,fill=IncCat))+ 768 | geom_bar(aes(y=Proportion),stat="identity",position = "dodge")+ 769 | scale_fill_manual(values=c("grey","darkviolet","orange","Red"),name="Increase Category")+ 770 | geom_text(aes(label=paste0(Proportion*100, "%")),position = position_dodge(width=1), angle=45)+ 771 | scale_y_continuous(labels=scales::percent)+ 772 | xlab("Gender")+ 773 | ggtitle("\n\nIncrease Category Proportion by Gender, Age 25 or Younger", 774 | subtitle="The Group Total is the sample proportion\nCaution: For the Large Increase, n=11")+ 775 | theme_minimal(base_size=20,base_line_size=11/22) 776 | 777 | gender.25under.chart 778 | ``` 779 | 780 | ```{r Gender Over 25 Chart} 781 | FP_gendergt25_df<-md%>% 782 | filter(age>25) 783 | FP_gendergt25<-CheckPropFun("gender",FP_gendergt25_df) 784 | kable(FP_gendergt25,align = "c") %>% 785 | kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))%>% 786 | add_header_above(c("Variable" = 1, 787 | "Population Proportion" = 1, 788 | "Increase Category" = 3, 789 | "Significant Diffs"=2))%>% 790 | add_header_above(c("\n\nProportion Table for gender, age>25"=7)) 791 | 792 | FP_gender_longgt25<-gather(FP_gendergt25,"Increase Category","Proportion",`Group Total`:`Large Increase`) 793 | FP_gender_longgt25<-transform(FP_gender_longgt25, 794 | IncCat=factor(`Increase Category`, 795 | levels=c("Group Total", 796 | "Decreased or No Change", 797 | "Small Increase", 798 | "Large Increase"))) 799 | 800 | gender.greater25.chart <- ggplot(FP_gender_longgt25,aes(x=gender,y=Proportion,fill=IncCat))+ 801 | geom_bar(aes(y=Proportion),stat="identity",position = "dodge")+ 802 | xlab("Gender") + 803 | scale_y_continuous(labels=scales::percent) + 804 | scale_fill_manual(values=c("grey","darkviolet","orange","Red"),name="Increase Category")+ 805 | ggtitle("\n\nIncrease Category Proportion by Gender, Older than 25", 806 | subtitle="The Group Total is the sample proportion")+ 807 | theme_minimal(base_size=20,base_line_size=11/22)+ 808 | geom_text(aes(label=paste0(Proportion*100, "%")),position = position_dodge(width=1), angle=45) 809 | 810 | gender.greater25.chart 811 | ``` 812 | 813 | #### Age of Oldest Operator 814 | 815 | ```{r Checking Age, include=TRUE} 816 | FP_age<-CheckPropFun("age_bucket",md) 817 | age.distribution.chart <- ggplot(md,aes(x=age,color=IncCat))+geom_density()+ scale_color_manual(values=c("darkviolet","Red","orange"),name="Increase Category")+ 818 | ggtitle("\n\nAge Distribution by Increase Category", 819 | subtitle="Grey is the sample density, we expect all categories to track with the sample")+ 820 | ylab("Density")+ 821 | xlab("Age") + 822 | theme_minimal(base_size=20,base_line_size=11/22)+ 823 | geom_density(aes(x=age), color="grey") 824 | 825 | md %>% filter(gender == "F") %>% filter(county == "BALTIMORE") %>% filter(age == 42) %>% select(current_premium, indicated_premium, selected_premium, IncDivCur, SelDivCur) 826 | 827 | age.distribution.chart 828 | ``` 829 | 830 | ```{r} 831 | kable(FP_age,align = "c") %>% 832 | kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))%>% 833 | add_header_above(c("Variable" = 1, 834 | "Population Proportion" = 1, 835 | "Increase Category" = 3, 836 | "Significant Diffs"=2))%>% 837 | add_header_above(c("\n\nProportion Table for Stat.Area"=7)) 838 | 839 | 840 | FP_age$mid<-c(28,51,70) 841 | category_by_median_income <- ggplot(FP_age,aes(x=mid))+xlab("midpoints of the buckets")+ 842 | geom_line(aes(y=`Group Total`),col="grey")+ 843 | geom_line(aes(y=`Decreased or No Change`),col="darkviolet")+ 844 | geom_line(aes(y=`Small Increase`),col="orange")+ 845 | geom_line(aes(y=`Large Increase`),col="red")+ 846 | ggtitle("\n\nIncrease Category Proportion by median income", 847 | subtitle="Grey is the sample proportion, we expect all categories to track with the sample")+ 848 | ylab("Proportion")+ 849 | theme_minimal(base_size=20,base_line_size=11/22) 850 | ``` 851 | 852 | ```{r} 853 | FP_age_long<-gather(FP_age,"Increase Category","Proportion",`Group Total`:`Large Increase`) 854 | FP_age_long<-transform(FP_age_long, 855 | IncCat=factor(`Increase Category`, 856 | levels=c("Group Total", 857 | "Decreased or No Change", 858 | "Small Increase", 859 | "Large Increase"))) 860 | 861 | age.category.chart <- ggplot(FP_age_long,aes(x=age_bucket,y=Proportion,fill=IncCat))+ 862 | geom_bar(aes(y=Proportion),stat="identity",position = "dodge")+ 863 | scale_fill_manual(values=c("grey","darkviolet","orange","Red"),name="Increase Category")+ 864 | geom_text(aes(label=paste0(Proportion*100, "%")),position = position_dodge(width=1), angle=45)+ 865 | ggtitle("\n\nIncrease Category Proportion by Age Group", 866 | subtitle="The Group Total is the sample proportion")+ 867 | theme_minimal(base_size=20,base_line_size=11/22)+ 868 | scale_y_continuous(labels=scales::percent)+ 869 | xlab("Age Group") 870 | 871 | age.category.chart 872 | ``` 873 | 874 | ```{r} 875 | older <- md %>% filter(age_bucket == "63+") 876 | 877 | paste0('Of the ', nrow(older),' customers in that age group,') 878 | 879 | paste0( nrow((filter(older, indicated_premium < current_premium))) / nrow(older) * 100,'% were owed discounts.') 880 | 881 | paste0("These customers were owed a median discount of $", median((filter(older, indicated_premium < current_premium))$IndMinCur) * -1," to reach their ideal price,") 882 | paste0("but would have received a median transition discount of a paltry $", median((filter(older, indicated_premium < current_premium))$SelMinCur) * -1,".") 883 | 884 | ``` 885 | 886 | 887 | #### Racial Composition of Zip Code 888 | 889 | ```{r Checking percentage_nonwhite, include=TRUE} 890 | pnw.chart <- ggplot(md,aes(x=percentage_nonwhite,color=IncCat))+geom_density()+ scale_color_manual(values=c("darkviolet","Red","orange"),name="Increase Category")+ 891 | ggtitle("\n\nPercentage Nonwhite Distribution by Increase Category", 892 | subtitle="Grey is the sample density, we expect all categories to track with the sample")+ 893 | ylab("Density")+ 894 | xlab("Percentage Non-White")+ 895 | scale_x_continuous(labels=scales::percent)+ 896 | theme_minimal(base_size=20,base_line_size=11/22)+ 897 | geom_density(aes(x=percentage_nonwhite), color="grey") 898 | 899 | pnw.chart 900 | ``` 901 | 902 | ```{r} 903 | 904 | FP_pnw<-CheckPropFun("PNW_bucket4",md) 905 | 906 | FP_pnw2<-as.data.frame(FP_pnw[order(FP_pnw$PNW_bucket4),]) 907 | 908 | kable(FP_pnw2,align = "c") %>% 909 | kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))%>% 910 | add_header_above(c("RN"=1, 911 | "Variable" = 1, 912 | "Population Proportion" = 1, 913 | "Increase Category" = 3, 914 | "Significant Diffs"=2))%>% 915 | add_header_above(c("\n\nProportion Table for percentage_nonwhite"=8)) 916 | 917 | 918 | FP_pnw2$mid<-c(.125,0.3725,0.635,0.875) 919 | inc_category_proportion_by_pct <- ggplot(FP_pnw2,aes(x=mid))+xlab("midpoints of the buckets")+ 920 | geom_line(aes(y=`Group Total`),col="grey")+ 921 | geom_line(aes(y=`Decreased or No Change`),col="darkviolet")+ 922 | geom_line(aes(y=`Small Increase`),col="orange")+ 923 | geom_line(aes(y=`Large Increase`),col="red")+ 924 | ggtitle("\n\nIncrease Category Proportion by Percentage Non-White", 925 | subtitle="Grey is the sample proportion, we expect all categories to track with the sample")+ 926 | ylab("Proportion")+ 927 | theme_minimal(base_size=20,base_line_size=11/22) 928 | 929 | FP_pnw_long2<-gather(FP_pnw2,"Increase Category","Proportion",`Group Total`:`Large Increase`) 930 | FP_pnw_long2<-transform(FP_pnw_long2, 931 | IncCat=factor(`Increase Category`, 932 | levels=c("Group Total", 933 | "Decreased or No Change", 934 | "Small Increase", 935 | "Large Increase"))) 936 | ``` 937 | 938 | ```{r} 939 | pnw.proportion.chart <- ggplot(FP_pnw_long2,aes(x=PNW_bucket4,y=Proportion,fill=IncCat))+ 940 | geom_bar(aes(y=Proportion),stat="identity",position = "dodge")+ 941 | scale_fill_manual(values=c("grey","darkviolet","orange","Red"),name="Increase Category")+ 942 | ggtitle("\n\nIncrease Category Proportion by Percentage Non-White", 943 | subtitle="The Group Total is the sample proportion")+ 944 | geom_text(aes(label=paste0(Proportion*100, "%")),position = position_dodge(width=1), angle=70)+ 945 | scale_y_continuous(labels=scales::percent)+ 946 | theme_minimal(base_size=20,base_line_size=11/22)+ 947 | theme(axis.text.x = element_text(angle = 45))+ 948 | xlab("Percentage Non-White") 949 | 950 | pnw.proportion.chart 951 | ``` 952 | 953 | ## Concluding Discussion 954 | 955 | ```{r} 956 | overpay <- md %>% filter(indicated_premium < current_premium) 957 | 958 | ## While those customers were own an average of... 959 | mean(overpay$IndMinCur) 960 | mean(overpay$SelMinCur) 961 | 962 | ## While those customers were owed a median 963 | median(overpay$IndMinCur) 964 | median(overpay$SelMinCur) 965 | 966 | ### 967 | overpay %>% group_by(age_bucket) %>% summarise(n = n()) 968 | 969 | md %>% filter(age_bucket == "63+") %>% group_by(IncCat) %>% summarise(n = n(), mean_sel = mean(SelMinCur), mean_ind = mean(IndMinCur), median_sel = median(SelMinCur), median_ind = median(IndMinCur)) 970 | ``` 971 | 972 | ## Acknowledgments --------------------------------------------------------------------------------