Geographically weighted regressions for prioritizing educational planning, policies, and interventions - Supporting code.ipynb

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    "# Geographically weighted regressions for prioritizing educational planning, policies, and interventions - Supporting code\n",
    "\n",
    "This code accompanies the paper \"Geographically Weighted Regressions for prioritizing educational planning, policies, and interventions\", by the Development Unit from UNESCO Institute for Educational Planning. The document can be downloaded from https://unesdoc.unesco.org/ark:/48223/pf0000380202.locale=en, and the accompanying data can be obtained here: https://box.iiep.unesco.org/index.php/s/crXXYgk35YqEpr4\n",
    "\n",
    "Note that the information used for the construction of this example is available free of charge, but a permission needs to be requested by emailing datoscede@uniandes.edu.co. Open receiving permission from this email, it can be forwarded to development@iiep.unesco.org, and the password for accessing to the folder will be provided. For more information on the data source, visit https://datoscede.uniandes.edu.co/es/catalogo-de-microdata\n",
    "\n",
    "Installing all the packages that will be needed, and loading them into the R kernel. Note that install.packages has been commented out, and should be uncommented as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: maptools\n",
      "\n",
      "Loading required package: sp\n",
      "\n",
      "Checking rgeos availability: TRUE\n",
      "\n",
      "Loading required package: robustbase\n",
      "\n",
      "Loading required package: Rcpp\n",
      "\n",
      "Loading required package: spatialreg\n",
      "\n",
      "Loading required package: spData\n",
      "\n",
      "To access larger datasets in this package, install the spDataLarge\n",
      "package with: `install.packages('spDataLarge',\n",
      "repos='https://nowosad.github.io/drat/', type='source')`\n",
      "\n",
      "Loading required package: Matrix\n",
      "\n",
      "Registered S3 methods overwritten by 'spatialreg':\n",
      "  method                   from \n",
      "  residuals.stsls          spdep\n",
      "  deviance.stsls           spdep\n",
      "  coef.stsls               spdep\n",
      "  print.stsls              spdep\n",
      "  summary.stsls            spdep\n",
      "  print.summary.stsls      spdep\n",
      "  residuals.gmsar          spdep\n",
      "  deviance.gmsar           spdep\n",
      "  coef.gmsar               spdep\n",
      "  fitted.gmsar             spdep\n",
      "  print.gmsar              spdep\n",
      "  summary.gmsar            spdep\n",
      "  print.summary.gmsar      spdep\n",
      "  print.lagmess            spdep\n",
      "  summary.lagmess          spdep\n",
      "  print.summary.lagmess    spdep\n",
      "  residuals.lagmess        spdep\n",
      "  deviance.lagmess         spdep\n",
      "  coef.lagmess             spdep\n",
      "  fitted.lagmess           spdep\n",
      "  logLik.lagmess           spdep\n",
      "  fitted.SFResult          spdep\n",
      "  print.SFResult           spdep\n",
      "  fitted.ME_res            spdep\n",
      "  print.ME_res             spdep\n",
      "  print.lagImpact          spdep\n",
      "  plot.lagImpact           spdep\n",
      "  summary.lagImpact        spdep\n",
      "  HPDinterval.lagImpact    spdep\n",
      "  print.summary.lagImpact  spdep\n",
      "  print.sarlm              spdep\n",
      "  summary.sarlm            spdep\n",
      "  residuals.sarlm          spdep\n",
      "  deviance.sarlm           spdep\n",
      "  coef.sarlm               spdep\n",
      "  vcov.sarlm               spdep\n",
      "  fitted.sarlm             spdep\n",
      "  logLik.sarlm             spdep\n",
      "  anova.sarlm              spdep\n",
      "  predict.sarlm            spdep\n",
      "  print.summary.sarlm      spdep\n",
      "  print.sarlm.pred         spdep\n",
      "  as.data.frame.sarlm.pred spdep\n",
      "  residuals.spautolm       spdep\n",
      "  deviance.spautolm        spdep\n",
      "  coef.spautolm            spdep\n",
      "  fitted.spautolm          spdep\n",
      "  print.spautolm           spdep\n",
      "  summary.spautolm         spdep\n",
      "  logLik.spautolm          spdep\n",
      "  print.summary.spautolm   spdep\n",
      "  print.WXImpact           spdep\n",
      "  summary.WXImpact         spdep\n",
      "  print.summary.WXImpact   spdep\n",
      "  predict.SLX              spdep\n",
      "\n",
      "Welcome to GWmodel version 2.1-4.\n",
      "The new version of GWmodel 2.1-4 now is ready\n",
      "Loading required package: fields\n",
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      "\n",
      "Spam version 2.5-1 (2019-12-12) is loaded.\n",
      "Type 'help( Spam)' or 'demo( spam)' for a short introduction \n",
      "and overview of this package.\n",
      "Help for individual functions is also obtained by adding the\n",
      "suffix '.spam' to the function name, e.g. 'help( chol.spam)'.\n",
      "\n",
      "\n",
      "Attaching package: 'spam'\n",
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      "\n",
      "    det\n",
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      "\n",
      "    backsolve, forwardsolve\n",
      "\n",
      "\n",
      "See https://github.com/NCAR/Fields for\n",
      " an extensive vignette, other supplements and source code \n",
      "\n",
      "Loading required package: lars\n",
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      "Loaded lars 1.2\n",
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      "Loading required package: randomForest\n",
      "\n",
      "randomForest 4.6-14\n",
      "\n",
      "Type rfNews() to see new features/changes/bug fixes.\n",
      "\n",
      "Loading required package: raster\n",
      "\n",
      "Loading required package: rgdal\n",
      "\n",
      "rgdal: version: 1.5-16, (SVN revision 1050)\n",
      "Geospatial Data Abstraction Library extensions to R successfully loaded\n",
      "Loaded GDAL runtime: GDAL 3.0.4, released 2020/01/28\n",
      "Path to GDAL shared files: C:/Users/g.vargas/Documents/R/R-4.0.2/library/rgdal/gdal\n",
      "GDAL binary built with GEOS: TRUE \n",
      "Loaded PROJ runtime: Rel. 6.3.1, February 10th, 2020, [PJ_VERSION: 631]\n",
      "Path to PROJ shared files: C:/Users/g.vargas/Documents/R/R-4.0.2/library/rgdal/proj\n",
      "Linking to sp version:1.4-2\n",
      "To mute warnings of possible GDAL/OSR exportToProj4() degradation,\n",
      "use options(\"rgdal_show_exportToProj4_warnings\"=\"none\") before loading rgdal.\n",
      "\n",
      "Registered S3 method overwritten by 'ape':\n",
      "  method   from \n",
      "  plot.mst spdep\n",
      "\n",
      "\n",
      "Attaching package: 'ape'\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:raster':\n",
      "\n",
      "    rotate, zoom\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: 'tidyr'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:raster':\n",
      "\n",
      "    extract\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:Matrix':\n",
      "\n",
      "    expand, pack, unpack\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: 'MASS'\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:raster':\n",
      "\n",
      "    area, select\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: 'dplyr'\n",
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      "\n",
      "    select\n",
      "\n",
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      "The following objects are masked from 'package:raster':\n",
      "\n",
      "    intersect, select, union\n",
      "\n",
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      "The following object is masked from 'package:randomForest':\n",
      "\n",
      "    combine\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:stats':\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: 'spatialEco'\n",
      "\n",
      "\n",
      "The following object is masked from 'package:dplyr':\n",
      "\n",
      "    combine\n",
      "\n",
      "\n",
      "The following object is masked from 'package:Compositional':\n",
      "\n",
      "    divergence\n",
      "\n",
      "\n",
      "The following object is masked from 'package:raster':\n",
      "\n",
      "    shift\n",
      "\n",
      "\n",
      "The following object is masked from 'package:randomForest':\n",
      "\n",
      "    combine\n",
      "\n",
      "\n",
      "The following object is masked from 'package:grid':\n",
      "\n",
      "    explode\n",
      "\n",
      "\n",
      "The following object is masked from 'package:spData':\n",
      "\n",
      "    elev\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "options(warn = -1) \n",
    "#install.packages(\"GWmodel\")\n",
    "library(\"GWmodel\") \n",
    "#install.packages(\"gwrr\")\n",
    "library(\"gwrr\")\n",
    "#install.packages(\"sp\")\n",
    "library(\"sp\")\n",
    "#install.packages(\"gstat\")\n",
    "library(\"gstat\")\n",
    "#install.packages(\"ModelMap\")\n",
    "library(\"ModelMap\")\n",
    "#install.packages(\"RColorBrewer\")\n",
    "library(\"RColorBrewer\")\n",
    "#install.packages(\"ape\")\n",
    "library(\"ape\")\n",
    "#install.packages(\"usdm\")\n",
    "library(\"usdm\")\n",
    "#install.packages(\"rgdal\")\n",
    "library(\"rgdal\")\n",
    "#install.packages(\"tidyr\")\n",
    "library(\"tidyr\")\n",
    "#install.packages(\"MASS\")\n",
    "library(\"MASS\")\n",
    "#install.packages(\"Compositional\")\n",
    "library(\"Compositional\")\n",
    "#install.packages(\"dplyr\")\n",
    "library(\"dplyr\")\n",
    "#install.packages(\"spatialEco\")\n",
    "library(\"spatialEco\")\n",
    "#install.packages(\"broom\")\n",
    "library(\"broom\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determining the working directory, which should have the same structure as the one found in in the Replication folder.\n",
    "This needs to be changes to the user's folder. Note that Windows uses backslashed (\\), while Mac uses forward-slash (/). The code needs to be changed accordingly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "setwd(\"C:\\\\Users\\\\g.vargas\\\\BOX\\\\IIEP_MyProjects\\\\MP_01000298_RND_SDA\\\\WorkFiles_Experts\\\\298-Issue-Papers\\\\298-Issue-Paper-GWR\\\\Replication files\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the data extracted from QGIS\n",
    "\n",
    "Note that the file should be saved under the Data\\Colombia folder. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OGR data source with driver: ESRI Shapefile \n",
      "Source: \"C:\\Users\\g.vargas\\BOX\\IIEP_MyProjects\\MP_01000298_RND_SDA\\WorkFiles_Experts\\298-Issue-Papers\\298-Issue-Paper-GWR\\Replication files\\Data\\Colombia\", layer: \"GWR - Colombia\"\n",
      "with 1117 features\n",
      "It has 345 fields\n",
      "Integer64 fields read as strings:  Key CV_ano CV_Violenc CV_conflic CV_confl_1 CV_confl_2 CV_poblind CV_ocupesp CV_asalt_p CV_incur_p CV_terrori CV_secuest CV_pirater CV_reten_i CV_hostig_ CV_ataq_in CV_ataq_ae CV_acc_sub CV_embosca CV_cont_ar CV_homic_v CV_homic_c CV_homicid CV_homi_tr CV_captura CV_captu_G CV_captu_E CV_captu_F CV_coca CV_o_amena CV_o_despl CV_d_amena CV_d_despl CV_desplaz CV_despl_1 CV_hurto_p CV_hurto_r CV_hurto_a CV_hurto_m CV_hurto_c CV_hurto_e CV_hurto_v CV_hurto E_ano E_pobsimpl E_pobsim_1 E_t_establ E_t_esta_1 E_t_esta_2 E_jornada_ E_jornad_1 E_jornad_2 E_jornad_3 E_jornad_4 E_jornad_5 E_jornad_6 E_jornad_7 E_jornad_8 E_admin_to E_admin__1 E_admin__2 E_admin__3 E_admin_m E_admin_h E_docen_to E_docen_of E_docen_ru E_docen_ur E_docen_pr E_docen__1 E_docen_se E_docen_me E_docen_mp E_docen__2 E_docen_ms E_docen_mm E_docen_hp E_docen__3 E_docen_hs E_docen_hm E_alumn_to E_alumn_of E_alumn_ru E_alumn_ur E_alumn_pr E_alumn__1 E_alumn_se E_alumn_me E_alumn__2 E_alumn__3 E_alumn__4 E_alumn__5 E_alumn__6 E_alumn__7 E_alumn__8 E_alumn__9 E_alumn_10 E_alumn_11 E_alumn_12 E_alumn_13 E_alumn_mp E_alumn_14 E_alumn_ms E_alumn_mm E_alumn_mj E_alumn_hp E_alumn_15 E_alumn_hs E_alumn_hm E_alumn_hj E_n_icfes E_col_tota E_col_to_1 E_col_to_2 E_col_B E_col_C E_col_D E_col_B_o E_col_C_o E_col_D_o E_col_B_ca E_col_C_ca E_col_D_ca GC_coddept GC_codprov GC_ano GC_ao_crea GC_gandina GC_gcaribe GC_gpacifi GC_gorinoq GC_gamazon GC_pobl_ru GC_pobl_ur GC_pobl_to GC_sisben3 GC_sisbe_1 GC_sisbe_2 GC_sisbe_3 GC_sisbe_4 GC_sisbe_5 GC_sisbe_6 GC_sisbe_7 GC_sisbe_8 GC_sisbe_9 GC_areaofi GC_areao_1 GC_altura GC_codmdo GC_mercado G_ano G_DF_p_nal G_DI_p_nal G_SGP_ribe G_IGA_plan G_IGA_sece HS_Aï¿½o HS_Nï¿½mer HS_Nï¿½m_1 HS_Nï¿½m_2 HS_Nï¿½m_3 HS_Nï¿½m_4 HS_Registr HS_Usuario HS_Usuar_1 HS_Total d \n",
      " The final names of the variables that were imported are the following:"
     ]
    },
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       "</style>\n",
       "<ol class=list-inline><li>'fid'</li><li>'admin0Name'</li><li>'admin0Pcod'</li><li>'admin1Name'</li><li>'admin1Pcod'</li><li>'admin2Name'</li><li>'admin2Pcod'</li><li>'admin2RefN'</li><li>'admin2AltN'</li><li>'admin2Al_1'</li><li>'date'</li><li>'validOn'</li><li>'validTo'</li><li>'Shape_Leng'</li><li>'Shape_Area'</li><li>'Key'</li><li>'Long'</li><li>'Lat'</li><li>'MurderRate'</li><li>'Poverty'</li><li>'ProxyGDP'</li><li>'ICFES_GPI'</li><li>'TransEducP'</li><li>'TransAlimE'</li><li>'SISBEN1PC'</li><li>'CV_fid'</li><li>'CV_ano'</li><li>'CV_Violenc'</li><li>'CV_conflic'</li><li>'CV_confl_1'</li><li>'CV_confl_2'</li><li>'CV_poblind'</li><li>'CV_ocupesp'</li><li>'CV_asalt_p'</li><li>'CV_incur_p'</li><li>'CV_terrori'</li><li>'CV_secuest'</li><li>'CV_pirater'</li><li>'CV_reten_i'</li><li>'CV_hostig_'</li><li>'CV_ataq_in'</li><li>'CV_ataq_ae'</li><li>'CV_acc_sub'</li><li>'CV_embosca'</li><li>'CV_cont_ar'</li><li>'CV_homic_v'</li><li>'CV_homic_c'</li><li>'CV_homicid'</li><li>'CV_homi_tr'</li><li>'CV_captura'</li><li>'CV_captu_G'</li><li>'CV_captu_E'</li><li>'CV_captu_F'</li><li>'CV_coca'</li><li>'CV_o_amena'</li><li>'CV_o_despl'</li><li>'CV_d_amena'</li><li>'CV_d_despl'</li><li>'CV_desplaz'</li><li>'CV_despl_1'</li><li>'CV_hurto_p'</li><li>'CV_hurto_r'</li><li>'CV_hurto_a'</li><li>'CV_hurto_m'</li><li>'CV_hurto_c'</li><li>'CV_hurto_e'</li><li>'CV_hurto_v'</li><li>'CV_hurto'</li><li>'E_fid'</li><li>'E_ano'</li><li>'E_pobsimpl'</li><li>'E_pobsim_1'</li><li>'E_t_establ'</li><li>'E_t_esta_1'</li><li>'E_t_esta_2'</li><li>'E_jornada_'</li><li>'E_jornad_1'</li><li>'E_jornad_2'</li><li>'E_jornad_3'</li><li>'E_jornad_4'</li><li>'E_jornad_5'</li><li>'E_jornad_6'</li><li>'E_jornad_7'</li><li>'E_jornad_8'</li><li>'E_admin_to'</li><li>'E_admin__1'</li><li>'E_admin__2'</li><li>'E_admin__3'</li><li>'E_admin_m'</li><li>'E_admin_h'</li><li>'E_docen_to'</li><li>'E_docen_of'</li><li>'E_docen_ru'</li><li>'E_docen_ur'</li><li>'E_docen_pr'</li><li>'E_docen__1'</li><li>'E_docen_se'</li><li>'E_docen_me'</li><li>'E_docen_mp'</li><li>'E_docen__2'</li><li>'E_docen_ms'</li><li>'E_docen_mm'</li><li>'E_docen_hp'</li><li>'E_docen__3'</li><li>'E_docen_hs'</li><li>'E_docen_hm'</li><li>'E_alumn_to'</li><li>'E_alumn_of'</li><li>'E_alumn_ru'</li><li>'E_alumn_ur'</li><li>'E_alumn_pr'</li><li>'E_alumn__1'</li><li>'E_alumn_se'</li><li>'E_alumn_me'</li><li>'E_alumn__2'</li><li>'E_alumn__3'</li><li>'E_alumn__4'</li><li>'E_alumn__5'</li><li>'E_alumn__6'</li><li>'E_alumn__7'</li><li>'E_alumn__8'</li><li>'E_alumn__9'</li><li>'E_alumn_10'</li><li>'E_alumn_11'</li><li>'E_alumn_12'</li><li>'E_alumn_13'</li><li>'E_alumn_mp'</li><li>'E_alumn_14'</li><li>'E_alumn_ms'</li><li>'E_alumn_mm'</li><li>'E_alumn_mj'</li><li>'E_alumn_hp'</li><li>'E_alumn_15'</li><li>'E_alumn_hs'</li><li>'E_alumn_hm'</li><li>'E_alumn_hj'</li><li>'E_s11_tota'</li><li>'E_s11_mate'</li><li>'E_s11_soci'</li><li>'E_s11_cien'</li><li>'E_s11_lect'</li><li>'E_s11_ingl'</li><li>'E_s11_to_1'</li><li>'E_s11_to_2'</li><li>'E_s11_to_3'</li><li>'E_s11_to_4'</li><li>'E_s11_to_5'</li><li>'E_s11_to_6'</li><li>'E_s11_to_7'</li><li>'E_s11_to_8'</li><li>'E_s11_to_9'</li><li>'E_s11_to10'</li><li>'E_s11_to11'</li><li>'E_s11_to12'</li><li>'E_s11_to13'</li><li>'E_s11_to14'</li><li>'E_s11_to15'</li><li>'E_s11_to16'</li><li>'E_s11_to17'</li><li>'E_s11_to18'</li><li>'E_n_icfes'</li><li>'E_col_tota'</li><li>'E_col_to_1'</li><li>'E_col_to_2'</li><li>'E_col_B'</li><li>'E_col_C'</li><li>'E_col_D'</li><li>'E_col_B_o'</li><li>'E_col_C_o'</li><li>'E_col_D_o'</li><li>'E_col_B_ca'</li><li>'E_col_C_ca'</li><li>'E_col_D_ca'</li><li>'GC_fid'</li><li>'GC_coddept'</li><li>'GC_codprov'</li><li>'GC_depto'</li><li>'GC_provinc'</li><li>'GC_municip'</li><li>'GC_ano'</li><li>'GC_ao_crea'</li><li>'GC_act_adm'</li><li>'GC_gandina'</li><li>'GC_gcaribe'</li><li>'GC_gpacifi'</li><li>'GC_gorinoq'</li><li>'GC_gamazon'</li><li>'GC_pobl_ru'</li><li>'GC_pobl_ur'</li><li>'GC_pobl_to'</li><li>'GC_indrura'</li><li>'GC_sisben3'</li><li>'GC_sisbe_1'</li><li>'GC_sisbe_2'</li><li>'GC_sisbe_3'</li><li>'GC_sisbe_4'</li><li>'GC_sisbe_5'</li><li>'GC_sisbe_6'</li><li>'GC_sisbe_7'</li><li>'GC_sisbe_8'</li><li>'GC_sisbe_9'</li><li>'GC_areaofi'</li><li>'GC_areao_1'</li><li>'GC_altura'</li><li>'GC_discapi'</li><li>'GC_dismdo'</li><li>'GC_disbogo'</li><li>'GC_codmdo'</li><li>'GC_mercado'</li><li>'GC_distanc'</li><li>'G_fid'</li><li>'G_ano'</li><li>'G_y_total'</li><li>'G_y_corr'</li><li>'G_y_corr_t'</li><li>'G_y_corr_1'</li><li>'G_y_corr_2'</li><li>'G_y_corr_3'</li><li>'G_y_corr_4'</li><li>'G_y_no_tri'</li><li>'G_y_transf'</li><li>'G_y_tran_1'</li><li>'G_y_tran_2'</li><li>'G_g_total'</li><li>'G_g_corr'</li><li>'G_g_func'</li><li>'G_g_func_p'</li><li>'G_g_func_g'</li><li>'G_g_func_t'</li><li>'G_g_int_de'</li><li>'G_deficit'</li><li>'G_y_cap'</li><li>'G_y_cap_re'</li><li>'G_y_cap_tr'</li><li>'G_y_cap_co'</li><li>'G_y_cap_ot'</li><li>'G_g_cap'</li><li>'G_g_cap_FB'</li><li>'G_g_cap_re'</li><li>'G_deficit_'</li><li>'G_finan'</li><li>'G_finan_cr'</li><li>'G_finan__1'</li><li>'G_finan__2'</li><li>'G_finan__3'</li><li>'G_DF_ing_f'</li><li>'G_DF_deuda'</li><li>'G_DF_ing_t'</li><li>'G_DF_ing_p'</li><li>'G_DF_gast_'</li><li>'G_DF_ahorr'</li><li>'G_DF_desem'</li><li>'G_DF_p_nal'</li><li>'G_DI_efica'</li><li>'G_DI_efici'</li><li>'G_DI_rlega'</li><li>'G_DI_capad'</li><li>'G_DI_gesti'</li><li>'G_DI_desem'</li><li>'G_DI_rango'</li><li>'G_DI_p_nal'</li><li>'G_categori'</li><li>'G_SGP_tota'</li><li>'G_SGP_educ'</li><li>'G_SGP_ed_1'</li><li>'G_SGP_ed_2'</li><li>'G_SGP_ed_3'</li><li>'G_SGP_ed_4'</li><li>'G_SGP_salu'</li><li>'G_SGP_sa_1'</li><li>'G_SGP_sa_2'</li><li>'G_SGP_sa_3'</li><li>'G_SGP_agua'</li><li>'G_SGP_prop'</li><li>'G_SGP_pr_1'</li><li>'G_SGP_pr_2'</li><li>'G_SGP_pr_3'</li><li>'G_SGP_pr_4'</li><li>'G_SGP_pr_5'</li><li>'G_SGP_alim'</li><li>'G_SGP_ribe'</li><li>'G_SGP_resg'</li><li>'G_SGP_fp'</li><li>'G_SGP_pinf'</li><li>'G_IGA_tota'</li><li>'G_IGA_org_'</li><li>'G_IGA_cont'</li><li>'G_IGA_meci'</li><li>'G_IGA_co_1'</li><li>'G_IGA_gest'</li><li>'G_IGA_exp_'</li><li>'G_IGA_visi'</li><li>'G_IGA_co_2'</li><li>'G_IGA_plan'</li><li>'G_IGA_comp'</li><li>'G_IGA_sui'</li><li>'G_IGA_sisb'</li><li>'G_IGA_sima'</li><li>'G_IGA_sist'</li><li>'G_IGA_fut'</li><li>'G_IGA_rega'</li><li>'G_IGA_sice'</li><li>'G_IGA_siga'</li><li>'G_IGA_sige'</li><li>'G_IGA_dial'</li><li>'G_IGA_gobi'</li><li>'G_IGA_gel_'</li><li>'G_IGA_ge_1'</li><li>'G_IGA_sece'</li><li>'G_IGA_si_1'</li><li>'G_IGA_suit'</li><li>'G_IGA_tran'</li><li>'G_IGA_si_2'</li><li>'G_IGA_si_3'</li><li>'G_IGA_si_4'</li><li>'G_IGA_si_5'</li><li>'G_IGA_aten'</li><li>'G_IGA_si_6'</li><li>'HS_fid'</li><li>'HS_Aï..o'</li><li>'HS_Nï..mer'</li><li>'HS_Cobertu'</li><li>'HS_Nï..m_1'</li><li>'HS_Nï..m_2'</li><li>'HS_Nï..m_3'</li><li>'HS_Nï..m_4'</li><li>'HS_Cober_1'</li><li>'HS_Cober_2'</li><li>'HS_Cober_3'</li><li>'HS_Cober_4'</li><li>'HS_Cober_5'</li><li>'HS_Cober_6'</li><li>'HS_Cober_7'</li><li>'HS_Cober_8'</li><li>'HS_Cober_9'</li><li>'HS_Registr'</li><li>'HS_Usuario'</li><li>'HS_Usuar_1'</li><li>'HS_Total.d'</li><li>'HS_Cober10'</li><li>'HS_Cober11'</li><li>'Vulnerabil'</li><li>'Threat'</li><li>'Lack_Respo'</li><li>'INFORM'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'fid'\n",
       "\\item 'admin0Name'\n",
       "\\item 'admin0Pcod'\n",
       "\\item 'admin1Name'\n",
       "\\item 'admin1Pcod'\n",
       "\\item 'admin2Name'\n",
       "\\item 'admin2Pcod'\n",
       "\\item 'admin2RefN'\n",
       "\\item 'admin2AltN'\n",
       "\\item 'admin2Al\\_1'\n",
       "\\item 'date'\n",
       "\\item 'validOn'\n",
       "\\item 'validTo'\n",
       "\\item 'Shape\\_Leng'\n",
       "\\item 'Shape\\_Area'\n",
       "\\item 'Key'\n",
       "\\item 'Long'\n",
       "\\item 'Lat'\n",
       "\\item 'MurderRate'\n",
       "\\item 'Poverty'\n",
       "\\item 'ProxyGDP'\n",
       "\\item 'ICFES\\_GPI'\n",
       "\\item 'TransEducP'\n",
       "\\item 'TransAlimE'\n",
       "\\item 'SISBEN1PC'\n",
       "\\item 'CV\\_fid'\n",
       "\\item 'CV\\_ano'\n",
       "\\item 'CV\\_Violenc'\n",
       "\\item 'CV\\_conflic'\n",
       "\\item 'CV\\_confl\\_1'\n",
       "\\item 'CV\\_confl\\_2'\n",
       "\\item 'CV\\_poblind'\n",
       "\\item 'CV\\_ocupesp'\n",
       "\\item 'CV\\_asalt\\_p'\n",
       "\\item 'CV\\_incur\\_p'\n",
       "\\item 'CV\\_terrori'\n",
       "\\item 'CV\\_secuest'\n",
       "\\item 'CV\\_pirater'\n",
       "\\item 'CV\\_reten\\_i'\n",
       "\\item 'CV\\_hostig\\_'\n",
       "\\item 'CV\\_ataq\\_in'\n",
       "\\item 'CV\\_ataq\\_ae'\n",
       "\\item 'CV\\_acc\\_sub'\n",
       "\\item 'CV\\_embosca'\n",
       "\\item 'CV\\_cont\\_ar'\n",
       "\\item 'CV\\_homic\\_v'\n",
       "\\item 'CV\\_homic\\_c'\n",
       "\\item 'CV\\_homicid'\n",
       "\\item 'CV\\_homi\\_tr'\n",
       "\\item 'CV\\_captura'\n",
       "\\item 'CV\\_captu\\_G'\n",
       "\\item 'CV\\_captu\\_E'\n",
       "\\item 'CV\\_captu\\_F'\n",
       "\\item 'CV\\_coca'\n",
       "\\item 'CV\\_o\\_amena'\n",
       "\\item 'CV\\_o\\_despl'\n",
       "\\item 'CV\\_d\\_amena'\n",
       "\\item 'CV\\_d\\_despl'\n",
       "\\item 'CV\\_desplaz'\n",
       "\\item 'CV\\_despl\\_1'\n",
       "\\item 'CV\\_hurto\\_p'\n",
       "\\item 'CV\\_hurto\\_r'\n",
       "\\item 'CV\\_hurto\\_a'\n",
       "\\item 'CV\\_hurto\\_m'\n",
       "\\item 'CV\\_hurto\\_c'\n",
       "\\item 'CV\\_hurto\\_e'\n",
       "\\item 'CV\\_hurto\\_v'\n",
       "\\item 'CV\\_hurto'\n",
       "\\item 'E\\_fid'\n",
       "\\item 'E\\_ano'\n",
       "\\item 'E\\_pobsimpl'\n",
       "\\item 'E\\_pobsim\\_1'\n",
       "\\item 'E\\_t\\_establ'\n",
       "\\item 'E\\_t\\_esta\\_1'\n",
       "\\item 'E\\_t\\_esta\\_2'\n",
       "\\item 'E\\_jornada\\_'\n",
       "\\item 'E\\_jornad\\_1'\n",
       "\\item 'E\\_jornad\\_2'\n",
       "\\item 'E\\_jornad\\_3'\n",
       "\\item 'E\\_jornad\\_4'\n",
       "\\item 'E\\_jornad\\_5'\n",
       "\\item 'E\\_jornad\\_6'\n",
       "\\item 'E\\_jornad\\_7'\n",
       "\\item 'E\\_jornad\\_8'\n",
       "\\item 'E\\_admin\\_to'\n",
       "\\item 'E\\_admin\\_\\_1'\n",
       "\\item 'E\\_admin\\_\\_2'\n",
       "\\item 'E\\_admin\\_\\_3'\n",
       "\\item 'E\\_admin\\_m'\n",
       "\\item 'E\\_admin\\_h'\n",
       "\\item 'E\\_docen\\_to'\n",
       "\\item 'E\\_docen\\_of'\n",
       "\\item 'E\\_docen\\_ru'\n",
       "\\item 'E\\_docen\\_ur'\n",
       "\\item 'E\\_docen\\_pr'\n",
       "\\item 'E\\_docen\\_\\_1'\n",
       "\\item 'E\\_docen\\_se'\n",
       "\\item 'E\\_docen\\_me'\n",
       "\\item 'E\\_docen\\_mp'\n",
       "\\item 'E\\_docen\\_\\_2'\n",
       "\\item 'E\\_docen\\_ms'\n",
       "\\item 'E\\_docen\\_mm'\n",
       "\\item 'E\\_docen\\_hp'\n",
       "\\item 'E\\_docen\\_\\_3'\n",
       "\\item 'E\\_docen\\_hs'\n",
       "\\item 'E\\_docen\\_hm'\n",
       "\\item 'E\\_alumn\\_to'\n",
       "\\item 'E\\_alumn\\_of'\n",
       "\\item 'E\\_alumn\\_ru'\n",
       "\\item 'E\\_alumn\\_ur'\n",
       "\\item 'E\\_alumn\\_pr'\n",
       "\\item 'E\\_alumn\\_\\_1'\n",
       "\\item 'E\\_alumn\\_se'\n",
       "\\item 'E\\_alumn\\_me'\n",
       "\\item 'E\\_alumn\\_\\_2'\n",
       "\\item 'E\\_alumn\\_\\_3'\n",
       "\\item 'E\\_alumn\\_\\_4'\n",
       "\\item 'E\\_alumn\\_\\_5'\n",
       "\\item 'E\\_alumn\\_\\_6'\n",
       "\\item 'E\\_alumn\\_\\_7'\n",
       "\\item 'E\\_alumn\\_\\_8'\n",
       "\\item 'E\\_alumn\\_\\_9'\n",
       "\\item 'E\\_alumn\\_10'\n",
       "\\item 'E\\_alumn\\_11'\n",
       "\\item 'E\\_alumn\\_12'\n",
       "\\item 'E\\_alumn\\_13'\n",
       "\\item 'E\\_alumn\\_mp'\n",
       "\\item 'E\\_alumn\\_14'\n",
       "\\item 'E\\_alumn\\_ms'\n",
       "\\item 'E\\_alumn\\_mm'\n",
       "\\item 'E\\_alumn\\_mj'\n",
       "\\item 'E\\_alumn\\_hp'\n",
       "\\item 'E\\_alumn\\_15'\n",
       "\\item 'E\\_alumn\\_hs'\n",
       "\\item 'E\\_alumn\\_hm'\n",
       "\\item 'E\\_alumn\\_hj'\n",
       "\\item 'E\\_s11\\_tota'\n",
       "\\item 'E\\_s11\\_mate'\n",
       "\\item 'E\\_s11\\_soci'\n",
       "\\item 'E\\_s11\\_cien'\n",
       "\\item 'E\\_s11\\_lect'\n",
       "\\item 'E\\_s11\\_ingl'\n",
       "\\item 'E\\_s11\\_to\\_1'\n",
       "\\item 'E\\_s11\\_to\\_2'\n",
       "\\item 'E\\_s11\\_to\\_3'\n",
       "\\item 'E\\_s11\\_to\\_4'\n",
       "\\item 'E\\_s11\\_to\\_5'\n",
       "\\item 'E\\_s11\\_to\\_6'\n",
       "\\item 'E\\_s11\\_to\\_7'\n",
       "\\item 'E\\_s11\\_to\\_8'\n",
       "\\item 'E\\_s11\\_to\\_9'\n",
       "\\item 'E\\_s11\\_to10'\n",
       "\\item 'E\\_s11\\_to11'\n",
       "\\item 'E\\_s11\\_to12'\n",
       "\\item 'E\\_s11\\_to13'\n",
       "\\item 'E\\_s11\\_to14'\n",
       "\\item 'E\\_s11\\_to15'\n",
       "\\item 'E\\_s11\\_to16'\n",
       "\\item 'E\\_s11\\_to17'\n",
       "\\item 'E\\_s11\\_to18'\n",
       "\\item 'E\\_n\\_icfes'\n",
       "\\item 'E\\_col\\_tota'\n",
       "\\item 'E\\_col\\_to\\_1'\n",
       "\\item 'E\\_col\\_to\\_2'\n",
       "\\item 'E\\_col\\_B'\n",
       "\\item 'E\\_col\\_C'\n",
       "\\item 'E\\_col\\_D'\n",
       "\\item 'E\\_col\\_B\\_o'\n",
       "\\item 'E\\_col\\_C\\_o'\n",
       "\\item 'E\\_col\\_D\\_o'\n",
       "\\item 'E\\_col\\_B\\_ca'\n",
       "\\item 'E\\_col\\_C\\_ca'\n",
       "\\item 'E\\_col\\_D\\_ca'\n",
       "\\item 'GC\\_fid'\n",
       "\\item 'GC\\_coddept'\n",
       "\\item 'GC\\_codprov'\n",
       "\\item 'GC\\_depto'\n",
       "\\item 'GC\\_provinc'\n",
       "\\item 'GC\\_municip'\n",
       "\\item 'GC\\_ano'\n",
       "\\item 'GC\\_ao\\_crea'\n",
       "\\item 'GC\\_act\\_adm'\n",
       "\\item 'GC\\_gandina'\n",
       "\\item 'GC\\_gcaribe'\n",
       "\\item 'GC\\_gpacifi'\n",
       "\\item 'GC\\_gorinoq'\n",
       "\\item 'GC\\_gamazon'\n",
       "\\item 'GC\\_pobl\\_ru'\n",
       "\\item 'GC\\_pobl\\_ur'\n",
       "\\item 'GC\\_pobl\\_to'\n",
       "\\item 'GC\\_indrura'\n",
       "\\item 'GC\\_sisben3'\n",
       "\\item 'GC\\_sisbe\\_1'\n",
       "\\item 'GC\\_sisbe\\_2'\n",
       "\\item 'GC\\_sisbe\\_3'\n",
       "\\item 'GC\\_sisbe\\_4'\n",
       "\\item 'GC\\_sisbe\\_5'\n",
       "\\item 'GC\\_sisbe\\_6'\n",
       "\\item 'GC\\_sisbe\\_7'\n",
       "\\item 'GC\\_sisbe\\_8'\n",
       "\\item 'GC\\_sisbe\\_9'\n",
       "\\item 'GC\\_areaofi'\n",
       "\\item 'GC\\_areao\\_1'\n",
       "\\item 'GC\\_altura'\n",
       "\\item 'GC\\_discapi'\n",
       "\\item 'GC\\_dismdo'\n",
       "\\item 'GC\\_disbogo'\n",
       "\\item 'GC\\_codmdo'\n",
       "\\item 'GC\\_mercado'\n",
       "\\item 'GC\\_distanc'\n",
       "\\item 'G\\_fid'\n",
       "\\item 'G\\_ano'\n",
       "\\item 'G\\_y\\_total'\n",
       "\\item 'G\\_y\\_corr'\n",
       "\\item 'G\\_y\\_corr\\_t'\n",
       "\\item 'G\\_y\\_corr\\_1'\n",
       "\\item 'G\\_y\\_corr\\_2'\n",
       "\\item 'G\\_y\\_corr\\_3'\n",
       "\\item 'G\\_y\\_corr\\_4'\n",
       "\\item 'G\\_y\\_no\\_tri'\n",
       "\\item 'G\\_y\\_transf'\n",
       "\\item 'G\\_y\\_tran\\_1'\n",
       "\\item 'G\\_y\\_tran\\_2'\n",
       "\\item 'G\\_g\\_total'\n",
       "\\item 'G\\_g\\_corr'\n",
       "\\item 'G\\_g\\_func'\n",
       "\\item 'G\\_g\\_func\\_p'\n",
       "\\item 'G\\_g\\_func\\_g'\n",
       "\\item 'G\\_g\\_func\\_t'\n",
       "\\item 'G\\_g\\_int\\_de'\n",
       "\\item 'G\\_deficit'\n",
       "\\item 'G\\_y\\_cap'\n",
       "\\item 'G\\_y\\_cap\\_re'\n",
       "\\item 'G\\_y\\_cap\\_tr'\n",
       "\\item 'G\\_y\\_cap\\_co'\n",
       "\\item 'G\\_y\\_cap\\_ot'\n",
       "\\item 'G\\_g\\_cap'\n",
       "\\item 'G\\_g\\_cap\\_FB'\n",
       "\\item 'G\\_g\\_cap\\_re'\n",
       "\\item 'G\\_deficit\\_'\n",
       "\\item 'G\\_finan'\n",
       "\\item 'G\\_finan\\_cr'\n",
       "\\item 'G\\_finan\\_\\_1'\n",
       "\\item 'G\\_finan\\_\\_2'\n",
       "\\item 'G\\_finan\\_\\_3'\n",
       "\\item 'G\\_DF\\_ing\\_f'\n",
       "\\item 'G\\_DF\\_deuda'\n",
       "\\item 'G\\_DF\\_ing\\_t'\n",
       "\\item 'G\\_DF\\_ing\\_p'\n",
       "\\item 'G\\_DF\\_gast\\_'\n",
       "\\item 'G\\_DF\\_ahorr'\n",
       "\\item 'G\\_DF\\_desem'\n",
       "\\item 'G\\_DF\\_p\\_nal'\n",
       "\\item 'G\\_DI\\_efica'\n",
       "\\item 'G\\_DI\\_efici'\n",
       "\\item 'G\\_DI\\_rlega'\n",
       "\\item 'G\\_DI\\_capad'\n",
       "\\item 'G\\_DI\\_gesti'\n",
       "\\item 'G\\_DI\\_desem'\n",
       "\\item 'G\\_DI\\_rango'\n",
       "\\item 'G\\_DI\\_p\\_nal'\n",
       "\\item 'G\\_categori'\n",
       "\\item 'G\\_SGP\\_tota'\n",
       "\\item 'G\\_SGP\\_educ'\n",
       "\\item 'G\\_SGP\\_ed\\_1'\n",
       "\\item 'G\\_SGP\\_ed\\_2'\n",
       "\\item 'G\\_SGP\\_ed\\_3'\n",
       "\\item 'G\\_SGP\\_ed\\_4'\n",
       "\\item 'G\\_SGP\\_salu'\n",
       "\\item 'G\\_SGP\\_sa\\_1'\n",
       "\\item 'G\\_SGP\\_sa\\_2'\n",
       "\\item 'G\\_SGP\\_sa\\_3'\n",
       "\\item 'G\\_SGP\\_agua'\n",
       "\\item 'G\\_SGP\\_prop'\n",
       "\\item 'G\\_SGP\\_pr\\_1'\n",
       "\\item 'G\\_SGP\\_pr\\_2'\n",
       "\\item 'G\\_SGP\\_pr\\_3'\n",
       "\\item 'G\\_SGP\\_pr\\_4'\n",
       "\\item 'G\\_SGP\\_pr\\_5'\n",
       "\\item 'G\\_SGP\\_alim'\n",
       "\\item 'G\\_SGP\\_ribe'\n",
       "\\item 'G\\_SGP\\_resg'\n",
       "\\item 'G\\_SGP\\_fp'\n",
       "\\item 'G\\_SGP\\_pinf'\n",
       "\\item 'G\\_IGA\\_tota'\n",
       "\\item 'G\\_IGA\\_org\\_'\n",
       "\\item 'G\\_IGA\\_cont'\n",
       "\\item 'G\\_IGA\\_meci'\n",
       "\\item 'G\\_IGA\\_co\\_1'\n",
       "\\item 'G\\_IGA\\_gest'\n",
       "\\item 'G\\_IGA\\_exp\\_'\n",
       "\\item 'G\\_IGA\\_visi'\n",
       "\\item 'G\\_IGA\\_co\\_2'\n",
       "\\item 'G\\_IGA\\_plan'\n",
       "\\item 'G\\_IGA\\_comp'\n",
       "\\item 'G\\_IGA\\_sui'\n",
       "\\item 'G\\_IGA\\_sisb'\n",
       "\\item 'G\\_IGA\\_sima'\n",
       "\\item 'G\\_IGA\\_sist'\n",
       "\\item 'G\\_IGA\\_fut'\n",
       "\\item 'G\\_IGA\\_rega'\n",
       "\\item 'G\\_IGA\\_sice'\n",
       "\\item 'G\\_IGA\\_siga'\n",
       "\\item 'G\\_IGA\\_sige'\n",
       "\\item 'G\\_IGA\\_dial'\n",
       "\\item 'G\\_IGA\\_gobi'\n",
       "\\item 'G\\_IGA\\_gel\\_'\n",
       "\\item 'G\\_IGA\\_ge\\_1'\n",
       "\\item 'G\\_IGA\\_sece'\n",
       "\\item 'G\\_IGA\\_si\\_1'\n",
       "\\item 'G\\_IGA\\_suit'\n",
       "\\item 'G\\_IGA\\_tran'\n",
       "\\item 'G\\_IGA\\_si\\_2'\n",
       "\\item 'G\\_IGA\\_si\\_3'\n",
       "\\item 'G\\_IGA\\_si\\_4'\n",
       "\\item 'G\\_IGA\\_si\\_5'\n",
       "\\item 'G\\_IGA\\_aten'\n",
       "\\item 'G\\_IGA\\_si\\_6'\n",
       "\\item 'HS\\_fid'\n",
       "\\item 'HS\\_Aï..o'\n",
       "\\item 'HS\\_Nï..mer'\n",
       "\\item 'HS\\_Cobertu'\n",
       "\\item 'HS\\_Nï..m\\_1'\n",
       "\\item 'HS\\_Nï..m\\_2'\n",
       "\\item 'HS\\_Nï..m\\_3'\n",
       "\\item 'HS\\_Nï..m\\_4'\n",
       "\\item 'HS\\_Cober\\_1'\n",
       "\\item 'HS\\_Cober\\_2'\n",
       "\\item 'HS\\_Cober\\_3'\n",
       "\\item 'HS\\_Cober\\_4'\n",
       "\\item 'HS\\_Cober\\_5'\n",
       "\\item 'HS\\_Cober\\_6'\n",
       "\\item 'HS\\_Cober\\_7'\n",
       "\\item 'HS\\_Cober\\_8'\n",
       "\\item 'HS\\_Cober\\_9'\n",
       "\\item 'HS\\_Registr'\n",
       "\\item 'HS\\_Usuario'\n",
       "\\item 'HS\\_Usuar\\_1'\n",
       "\\item 'HS\\_Total.d'\n",
       "\\item 'HS\\_Cober10'\n",
       "\\item 'HS\\_Cober11'\n",
       "\\item 'Vulnerabil'\n",
       "\\item 'Threat'\n",
       "\\item 'Lack\\_Respo'\n",
       "\\item 'INFORM'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'fid'\n",
       "2. 'admin0Name'\n",
       "3. 'admin0Pcod'\n",
       "4. 'admin1Name'\n",
       "5. 'admin1Pcod'\n",
       "6. 'admin2Name'\n",
       "7. 'admin2Pcod'\n",
       "8. 'admin2RefN'\n",
       "9. 'admin2AltN'\n",
       "10. 'admin2Al_1'\n",
       "11. 'date'\n",
       "12. 'validOn'\n",
       "13. 'validTo'\n",
       "14. 'Shape_Leng'\n",
       "15. 'Shape_Area'\n",
       "16. 'Key'\n",
       "17. 'Long'\n",
       "18. 'Lat'\n",
       "19. 'MurderRate'\n",
       "20. 'Poverty'\n",
       "21. 'ProxyGDP'\n",
       "22. 'ICFES_GPI'\n",
       "23. 'TransEducP'\n",
       "24. 'TransAlimE'\n",
       "25. 'SISBEN1PC'\n",
       "26. 'CV_fid'\n",
       "27. 'CV_ano'\n",
       "28. 'CV_Violenc'\n",
       "29. 'CV_conflic'\n",
       "30. 'CV_confl_1'\n",
       "31. 'CV_confl_2'\n",
       "32. 'CV_poblind'\n",
       "33. 'CV_ocupesp'\n",
       "34. 'CV_asalt_p'\n",
       "35. 'CV_incur_p'\n",
       "36. 'CV_terrori'\n",
       "37. 'CV_secuest'\n",
       "38. 'CV_pirater'\n",
       "39. 'CV_reten_i'\n",
       "40. 'CV_hostig_'\n",
       "41. 'CV_ataq_in'\n",
       "42. 'CV_ataq_ae'\n",
       "43. 'CV_acc_sub'\n",
       "44. 'CV_embosca'\n",
       "45. 'CV_cont_ar'\n",
       "46. 'CV_homic_v'\n",
       "47. 'CV_homic_c'\n",
       "48. 'CV_homicid'\n",
       "49. 'CV_homi_tr'\n",
       "50. 'CV_captura'\n",
       "51. 'CV_captu_G'\n",
       "52. 'CV_captu_E'\n",
       "53. 'CV_captu_F'\n",
       "54. 'CV_coca'\n",
       "55. 'CV_o_amena'\n",
       "56. 'CV_o_despl'\n",
       "57. 'CV_d_amena'\n",
       "58. 'CV_d_despl'\n",
       "59. 'CV_desplaz'\n",
       "60. 'CV_despl_1'\n",
       "61. 'CV_hurto_p'\n",
       "62. 'CV_hurto_r'\n",
       "63. 'CV_hurto_a'\n",
       "64. 'CV_hurto_m'\n",
       "65. 'CV_hurto_c'\n",
       "66. 'CV_hurto_e'\n",
       "67. 'CV_hurto_v'\n",
       "68. 'CV_hurto'\n",
       "69. 'E_fid'\n",
       "70. 'E_ano'\n",
       "71. 'E_pobsimpl'\n",
       "72. 'E_pobsim_1'\n",
       "73. 'E_t_establ'\n",
       "74. 'E_t_esta_1'\n",
       "75. 'E_t_esta_2'\n",
       "76. 'E_jornada_'\n",
       "77. 'E_jornad_1'\n",
       "78. 'E_jornad_2'\n",
       "79. 'E_jornad_3'\n",
       "80. 'E_jornad_4'\n",
       "81. 'E_jornad_5'\n",
       "82. 'E_jornad_6'\n",
       "83. 'E_jornad_7'\n",
       "84. 'E_jornad_8'\n",
       "85. 'E_admin_to'\n",
       "86. 'E_admin__1'\n",
       "87. 'E_admin__2'\n",
       "88. 'E_admin__3'\n",
       "89. 'E_admin_m'\n",
       "90. 'E_admin_h'\n",
       "91. 'E_docen_to'\n",
       "92. 'E_docen_of'\n",
       "93. 'E_docen_ru'\n",
       "94. 'E_docen_ur'\n",
       "95. 'E_docen_pr'\n",
       "96. 'E_docen__1'\n",
       "97. 'E_docen_se'\n",
       "98. 'E_docen_me'\n",
       "99. 'E_docen_mp'\n",
       "100. 'E_docen__2'\n",
       "101. 'E_docen_ms'\n",
       "102. 'E_docen_mm'\n",
       "103. 'E_docen_hp'\n",
       "104. 'E_docen__3'\n",
       "105. 'E_docen_hs'\n",
       "106. 'E_docen_hm'\n",
       "107. 'E_alumn_to'\n",
       "108. 'E_alumn_of'\n",
       "109. 'E_alumn_ru'\n",
       "110. 'E_alumn_ur'\n",
       "111. 'E_alumn_pr'\n",
       "112. 'E_alumn__1'\n",
       "113. 'E_alumn_se'\n",
       "114. 'E_alumn_me'\n",
       "115. 'E_alumn__2'\n",
       "116. 'E_alumn__3'\n",
       "117. 'E_alumn__4'\n",
       "118. 'E_alumn__5'\n",
       "119. 'E_alumn__6'\n",
       "120. 'E_alumn__7'\n",
       "121. 'E_alumn__8'\n",
       "122. 'E_alumn__9'\n",
       "123. 'E_alumn_10'\n",
       "124. 'E_alumn_11'\n",
       "125. 'E_alumn_12'\n",
       "126. 'E_alumn_13'\n",
       "127. 'E_alumn_mp'\n",
       "128. 'E_alumn_14'\n",
       "129. 'E_alumn_ms'\n",
       "130. 'E_alumn_mm'\n",
       "131. 'E_alumn_mj'\n",
       "132. 'E_alumn_hp'\n",
       "133. 'E_alumn_15'\n",
       "134. 'E_alumn_hs'\n",
       "135. 'E_alumn_hm'\n",
       "136. 'E_alumn_hj'\n",
       "137. 'E_s11_tota'\n",
       "138. 'E_s11_mate'\n",
       "139. 'E_s11_soci'\n",
       "140. 'E_s11_cien'\n",
       "141. 'E_s11_lect'\n",
       "142. 'E_s11_ingl'\n",
       "143. 'E_s11_to_1'\n",
       "144. 'E_s11_to_2'\n",
       "145. 'E_s11_to_3'\n",
       "146. 'E_s11_to_4'\n",
       "147. 'E_s11_to_5'\n",
       "148. 'E_s11_to_6'\n",
       "149. 'E_s11_to_7'\n",
       "150. 'E_s11_to_8'\n",
       "151. 'E_s11_to_9'\n",
       "152. 'E_s11_to10'\n",
       "153. 'E_s11_to11'\n",
       "154. 'E_s11_to12'\n",
       "155. 'E_s11_to13'\n",
       "156. 'E_s11_to14'\n",
       "157. 'E_s11_to15'\n",
       "158. 'E_s11_to16'\n",
       "159. 'E_s11_to17'\n",
       "160. 'E_s11_to18'\n",
       "161. 'E_n_icfes'\n",
       "162. 'E_col_tota'\n",
       "163. 'E_col_to_1'\n",
       "164. 'E_col_to_2'\n",
       "165. 'E_col_B'\n",
       "166. 'E_col_C'\n",
       "167. 'E_col_D'\n",
       "168. 'E_col_B_o'\n",
       "169. 'E_col_C_o'\n",
       "170. 'E_col_D_o'\n",
       "171. 'E_col_B_ca'\n",
       "172. 'E_col_C_ca'\n",
       "173. 'E_col_D_ca'\n",
       "174. 'GC_fid'\n",
       "175. 'GC_coddept'\n",
       "176. 'GC_codprov'\n",
       "177. 'GC_depto'\n",
       "178. 'GC_provinc'\n",
       "179. 'GC_municip'\n",
       "180. 'GC_ano'\n",
       "181. 'GC_ao_crea'\n",
       "182. 'GC_act_adm'\n",
       "183. 'GC_gandina'\n",
       "184. 'GC_gcaribe'\n",
       "185. 'GC_gpacifi'\n",
       "186. 'GC_gorinoq'\n",
       "187. 'GC_gamazon'\n",
       "188. 'GC_pobl_ru'\n",
       "189. 'GC_pobl_ur'\n",
       "190. 'GC_pobl_to'\n",
       "191. 'GC_indrura'\n",
       "192. 'GC_sisben3'\n",
       "193. 'GC_sisbe_1'\n",
       "194. 'GC_sisbe_2'\n",
       "195. 'GC_sisbe_3'\n",
       "196. 'GC_sisbe_4'\n",
       "197. 'GC_sisbe_5'\n",
       "198. 'GC_sisbe_6'\n",
       "199. 'GC_sisbe_7'\n",
       "200. 'GC_sisbe_8'\n",
       "201. 'GC_sisbe_9'\n",
       "202. 'GC_areaofi'\n",
       "203. 'GC_areao_1'\n",
       "204. 'GC_altura'\n",
       "205. 'GC_discapi'\n",
       "206. 'GC_dismdo'\n",
       "207. 'GC_disbogo'\n",
       "208. 'GC_codmdo'\n",
       "209. 'GC_mercado'\n",
       "210. 'GC_distanc'\n",
       "211. 'G_fid'\n",
       "212. 'G_ano'\n",
       "213. 'G_y_total'\n",
       "214. 'G_y_corr'\n",
       "215. 'G_y_corr_t'\n",
       "216. 'G_y_corr_1'\n",
       "217. 'G_y_corr_2'\n",
       "218. 'G_y_corr_3'\n",
       "219. 'G_y_corr_4'\n",
       "220. 'G_y_no_tri'\n",
       "221. 'G_y_transf'\n",
       "222. 'G_y_tran_1'\n",
       "223. 'G_y_tran_2'\n",
       "224. 'G_g_total'\n",
       "225. 'G_g_corr'\n",
       "226. 'G_g_func'\n",
       "227. 'G_g_func_p'\n",
       "228. 'G_g_func_g'\n",
       "229. 'G_g_func_t'\n",
       "230. 'G_g_int_de'\n",
       "231. 'G_deficit'\n",
       "232. 'G_y_cap'\n",
       "233. 'G_y_cap_re'\n",
       "234. 'G_y_cap_tr'\n",
       "235. 'G_y_cap_co'\n",
       "236. 'G_y_cap_ot'\n",
       "237. 'G_g_cap'\n",
       "238. 'G_g_cap_FB'\n",
       "239. 'G_g_cap_re'\n",
       "240. 'G_deficit_'\n",
       "241. 'G_finan'\n",
       "242. 'G_finan_cr'\n",
       "243. 'G_finan__1'\n",
       "244. 'G_finan__2'\n",
       "245. 'G_finan__3'\n",
       "246. 'G_DF_ing_f'\n",
       "247. 'G_DF_deuda'\n",
       "248. 'G_DF_ing_t'\n",
       "249. 'G_DF_ing_p'\n",
       "250. 'G_DF_gast_'\n",
       "251. 'G_DF_ahorr'\n",
       "252. 'G_DF_desem'\n",
       "253. 'G_DF_p_nal'\n",
       "254. 'G_DI_efica'\n",
       "255. 'G_DI_efici'\n",
       "256. 'G_DI_rlega'\n",
       "257. 'G_DI_capad'\n",
       "258. 'G_DI_gesti'\n",
       "259. 'G_DI_desem'\n",
       "260. 'G_DI_rango'\n",
       "261. 'G_DI_p_nal'\n",
       "262. 'G_categori'\n",
       "263. 'G_SGP_tota'\n",
       "264. 'G_SGP_educ'\n",
       "265. 'G_SGP_ed_1'\n",
       "266. 'G_SGP_ed_2'\n",
       "267. 'G_SGP_ed_3'\n",
       "268. 'G_SGP_ed_4'\n",
       "269. 'G_SGP_salu'\n",
       "270. 'G_SGP_sa_1'\n",
       "271. 'G_SGP_sa_2'\n",
       "272. 'G_SGP_sa_3'\n",
       "273. 'G_SGP_agua'\n",
       "274. 'G_SGP_prop'\n",
       "275. 'G_SGP_pr_1'\n",
       "276. 'G_SGP_pr_2'\n",
       "277. 'G_SGP_pr_3'\n",
       "278. 'G_SGP_pr_4'\n",
       "279. 'G_SGP_pr_5'\n",
       "280. 'G_SGP_alim'\n",
       "281. 'G_SGP_ribe'\n",
       "282. 'G_SGP_resg'\n",
       "283. 'G_SGP_fp'\n",
       "284. 'G_SGP_pinf'\n",
       "285. 'G_IGA_tota'\n",
       "286. 'G_IGA_org_'\n",
       "287. 'G_IGA_cont'\n",
       "288. 'G_IGA_meci'\n",
       "289. 'G_IGA_co_1'\n",
       "290. 'G_IGA_gest'\n",
       "291. 'G_IGA_exp_'\n",
       "292. 'G_IGA_visi'\n",
       "293. 'G_IGA_co_2'\n",
       "294. 'G_IGA_plan'\n",
       "295. 'G_IGA_comp'\n",
       "296. 'G_IGA_sui'\n",
       "297. 'G_IGA_sisb'\n",
       "298. 'G_IGA_sima'\n",
       "299. 'G_IGA_sist'\n",
       "300. 'G_IGA_fut'\n",
       "301. 'G_IGA_rega'\n",
       "302. 'G_IGA_sice'\n",
       "303. 'G_IGA_siga'\n",
       "304. 'G_IGA_sige'\n",
       "305. 'G_IGA_dial'\n",
       "306. 'G_IGA_gobi'\n",
       "307. 'G_IGA_gel_'\n",
       "308. 'G_IGA_ge_1'\n",
       "309. 'G_IGA_sece'\n",
       "310. 'G_IGA_si_1'\n",
       "311. 'G_IGA_suit'\n",
       "312. 'G_IGA_tran'\n",
       "313. 'G_IGA_si_2'\n",
       "314. 'G_IGA_si_3'\n",
       "315. 'G_IGA_si_4'\n",
       "316. 'G_IGA_si_5'\n",
       "317. 'G_IGA_aten'\n",
       "318. 'G_IGA_si_6'\n",
       "319. 'HS_fid'\n",
       "320. 'HS_Aï..o'\n",
       "321. 'HS_Nï..mer'\n",
       "322. 'HS_Cobertu'\n",
       "323. 'HS_Nï..m_1'\n",
       "324. 'HS_Nï..m_2'\n",
       "325. 'HS_Nï..m_3'\n",
       "326. 'HS_Nï..m_4'\n",
       "327. 'HS_Cober_1'\n",
       "328. 'HS_Cober_2'\n",
       "329. 'HS_Cober_3'\n",
       "330. 'HS_Cober_4'\n",
       "331. 'HS_Cober_5'\n",
       "332. 'HS_Cober_6'\n",
       "333. 'HS_Cober_7'\n",
       "334. 'HS_Cober_8'\n",
       "335. 'HS_Cober_9'\n",
       "336. 'HS_Registr'\n",
       "337. 'HS_Usuario'\n",
       "338. 'HS_Usuar_1'\n",
       "339. 'HS_Total.d'\n",
       "340. 'HS_Cober10'\n",
       "341. 'HS_Cober11'\n",
       "342. 'Vulnerabil'\n",
       "343. 'Threat'\n",
       "344. 'Lack_Respo'\n",
       "345. 'INFORM'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] \"fid\"        \"admin0Name\" \"admin0Pcod\" \"admin1Name\" \"admin1Pcod\"\n",
       "  [6] \"admin2Name\" \"admin2Pcod\" \"admin2RefN\" \"admin2AltN\" \"admin2Al_1\"\n",
       " [11] \"date\"       \"validOn\"    \"validTo\"    \"Shape_Leng\" \"Shape_Area\"\n",
       " [16] \"Key\"        \"Long\"       \"Lat\"        \"MurderRate\" \"Poverty\"   \n",
       " [21] \"ProxyGDP\"   \"ICFES_GPI\"  \"TransEducP\" \"TransAlimE\" \"SISBEN1PC\" \n",
       " [26] \"CV_fid\"     \"CV_ano\"     \"CV_Violenc\" \"CV_conflic\" \"CV_confl_1\"\n",
       " [31] \"CV_confl_2\" \"CV_poblind\" \"CV_ocupesp\" \"CV_asalt_p\" \"CV_incur_p\"\n",
       " [36] \"CV_terrori\" \"CV_secuest\" \"CV_pirater\" \"CV_reten_i\" \"CV_hostig_\"\n",
       " [41] \"CV_ataq_in\" \"CV_ataq_ae\" \"CV_acc_sub\" \"CV_embosca\" \"CV_cont_ar\"\n",
       " [46] \"CV_homic_v\" \"CV_homic_c\" \"CV_homicid\" \"CV_homi_tr\" \"CV_captura\"\n",
       " [51] \"CV_captu_G\" \"CV_captu_E\" \"CV_captu_F\" \"CV_coca\"    \"CV_o_amena\"\n",
       " [56] \"CV_o_despl\" \"CV_d_amena\" \"CV_d_despl\" \"CV_desplaz\" \"CV_despl_1\"\n",
       " [61] \"CV_hurto_p\" \"CV_hurto_r\" \"CV_hurto_a\" \"CV_hurto_m\" \"CV_hurto_c\"\n",
       " [66] \"CV_hurto_e\" \"CV_hurto_v\" \"CV_hurto\"   \"E_fid\"      \"E_ano\"     \n",
       " [71] \"E_pobsimpl\" \"E_pobsim_1\" \"E_t_establ\" \"E_t_esta_1\" \"E_t_esta_2\"\n",
       " [76] \"E_jornada_\" \"E_jornad_1\" \"E_jornad_2\" \"E_jornad_3\" \"E_jornad_4\"\n",
       " [81] \"E_jornad_5\" \"E_jornad_6\" \"E_jornad_7\" \"E_jornad_8\" \"E_admin_to\"\n",
       " [86] \"E_admin__1\" \"E_admin__2\" \"E_admin__3\" \"E_admin_m\"  \"E_admin_h\" \n",
       " [91] \"E_docen_to\" \"E_docen_of\" \"E_docen_ru\" \"E_docen_ur\" \"E_docen_pr\"\n",
       " [96] \"E_docen__1\" \"E_docen_se\" \"E_docen_me\" \"E_docen_mp\" \"E_docen__2\"\n",
       "[101] \"E_docen_ms\" \"E_docen_mm\" \"E_docen_hp\" \"E_docen__3\" \"E_docen_hs\"\n",
       "[106] \"E_docen_hm\" \"E_alumn_to\" \"E_alumn_of\" \"E_alumn_ru\" \"E_alumn_ur\"\n",
       "[111] \"E_alumn_pr\" \"E_alumn__1\" \"E_alumn_se\" \"E_alumn_me\" \"E_alumn__2\"\n",
       "[116] \"E_alumn__3\" \"E_alumn__4\" \"E_alumn__5\" \"E_alumn__6\" \"E_alumn__7\"\n",
       "[121] \"E_alumn__8\" \"E_alumn__9\" \"E_alumn_10\" \"E_alumn_11\" \"E_alumn_12\"\n",
       "[126] \"E_alumn_13\" \"E_alumn_mp\" \"E_alumn_14\" \"E_alumn_ms\" \"E_alumn_mm\"\n",
       "[131] \"E_alumn_mj\" \"E_alumn_hp\" \"E_alumn_15\" \"E_alumn_hs\" \"E_alumn_hm\"\n",
       "[136] \"E_alumn_hj\" \"E_s11_tota\" \"E_s11_mate\" \"E_s11_soci\" \"E_s11_cien\"\n",
       "[141] \"E_s11_lect\" \"E_s11_ingl\" \"E_s11_to_1\" \"E_s11_to_2\" \"E_s11_to_3\"\n",
       "[146] \"E_s11_to_4\" \"E_s11_to_5\" \"E_s11_to_6\" \"E_s11_to_7\" \"E_s11_to_8\"\n",
       "[151] \"E_s11_to_9\" \"E_s11_to10\" \"E_s11_to11\" \"E_s11_to12\" \"E_s11_to13\"\n",
       "[156] \"E_s11_to14\" \"E_s11_to15\" \"E_s11_to16\" \"E_s11_to17\" \"E_s11_to18\"\n",
       "[161] \"E_n_icfes\"  \"E_col_tota\" \"E_col_to_1\" \"E_col_to_2\" \"E_col_B\"   \n",
       "[166] \"E_col_C\"    \"E_col_D\"    \"E_col_B_o\"  \"E_col_C_o\"  \"E_col_D_o\" \n",
       "[171] \"E_col_B_ca\" \"E_col_C_ca\" \"E_col_D_ca\" \"GC_fid\"     \"GC_coddept\"\n",
       "[176] \"GC_codprov\" \"GC_depto\"   \"GC_provinc\" \"GC_municip\" \"GC_ano\"    \n",
       "[181] \"GC_ao_crea\" \"GC_act_adm\" \"GC_gandina\" \"GC_gcaribe\" \"GC_gpacifi\"\n",
       "[186] \"GC_gorinoq\" \"GC_gamazon\" \"GC_pobl_ru\" \"GC_pobl_ur\" \"GC_pobl_to\"\n",
       "[191] \"GC_indrura\" \"GC_sisben3\" \"GC_sisbe_1\" \"GC_sisbe_2\" \"GC_sisbe_3\"\n",
       "[196] \"GC_sisbe_4\" \"GC_sisbe_5\" \"GC_sisbe_6\" \"GC_sisbe_7\" \"GC_sisbe_8\"\n",
       "[201] \"GC_sisbe_9\" \"GC_areaofi\" \"GC_areao_1\" \"GC_altura\"  \"GC_discapi\"\n",
       "[206] \"GC_dismdo\"  \"GC_disbogo\" \"GC_codmdo\"  \"GC_mercado\" \"GC_distanc\"\n",
       "[211] \"G_fid\"      \"G_ano\"      \"G_y_total\"  \"G_y_corr\"   \"G_y_corr_t\"\n",
       "[216] \"G_y_corr_1\" \"G_y_corr_2\" \"G_y_corr_3\" \"G_y_corr_4\" \"G_y_no_tri\"\n",
       "[221] \"G_y_transf\" \"G_y_tran_1\" \"G_y_tran_2\" \"G_g_total\"  \"G_g_corr\"  \n",
       "[226] \"G_g_func\"   \"G_g_func_p\" \"G_g_func_g\" \"G_g_func_t\" \"G_g_int_de\"\n",
       "[231] \"G_deficit\"  \"G_y_cap\"    \"G_y_cap_re\" \"G_y_cap_tr\" \"G_y_cap_co\"\n",
       "[236] \"G_y_cap_ot\" \"G_g_cap\"    \"G_g_cap_FB\" \"G_g_cap_re\" \"G_deficit_\"\n",
       "[241] \"G_finan\"    \"G_finan_cr\" \"G_finan__1\" \"G_finan__2\" \"G_finan__3\"\n",
       "[246] \"G_DF_ing_f\" \"G_DF_deuda\" \"G_DF_ing_t\" \"G_DF_ing_p\" \"G_DF_gast_\"\n",
       "[251] \"G_DF_ahorr\" \"G_DF_desem\" \"G_DF_p_nal\" \"G_DI_efica\" \"G_DI_efici\"\n",
       "[256] \"G_DI_rlega\" \"G_DI_capad\" \"G_DI_gesti\" \"G_DI_desem\" \"G_DI_rango\"\n",
       "[261] \"G_DI_p_nal\" \"G_categori\" \"G_SGP_tota\" \"G_SGP_educ\" \"G_SGP_ed_1\"\n",
       "[266] \"G_SGP_ed_2\" \"G_SGP_ed_3\" \"G_SGP_ed_4\" \"G_SGP_salu\" \"G_SGP_sa_1\"\n",
       "[271] \"G_SGP_sa_2\" \"G_SGP_sa_3\" \"G_SGP_agua\" \"G_SGP_prop\" \"G_SGP_pr_1\"\n",
       "[276] \"G_SGP_pr_2\" \"G_SGP_pr_3\" \"G_SGP_pr_4\" \"G_SGP_pr_5\" \"G_SGP_alim\"\n",
       "[281] \"G_SGP_ribe\" \"G_SGP_resg\" \"G_SGP_fp\"   \"G_SGP_pinf\" \"G_IGA_tota\"\n",
       "[286] \"G_IGA_org_\" \"G_IGA_cont\" \"G_IGA_meci\" \"G_IGA_co_1\" \"G_IGA_gest\"\n",
       "[291] \"G_IGA_exp_\" \"G_IGA_visi\" \"G_IGA_co_2\" \"G_IGA_plan\" \"G_IGA_comp\"\n",
       "[296] \"G_IGA_sui\"  \"G_IGA_sisb\" \"G_IGA_sima\" \"G_IGA_sist\" \"G_IGA_fut\" \n",
       "[301] \"G_IGA_rega\" \"G_IGA_sice\" \"G_IGA_siga\" \"G_IGA_sige\" \"G_IGA_dial\"\n",
       "[306] \"G_IGA_gobi\" \"G_IGA_gel_\" \"G_IGA_ge_1\" \"G_IGA_sece\" \"G_IGA_si_1\"\n",
       "[311] \"G_IGA_suit\" \"G_IGA_tran\" \"G_IGA_si_2\" \"G_IGA_si_3\" \"G_IGA_si_4\"\n",
       "[316] \"G_IGA_si_5\" \"G_IGA_aten\" \"G_IGA_si_6\" \"HS_fid\"     \"HS_Aï..o\"  \n",
       "[321] \"HS_Nï..mer\" \"HS_Cobertu\" \"HS_Nï..m_1\" \"HS_Nï..m_2\" \"HS_Nï..m_3\"\n",
       "[326] \"HS_Nï..m_4\" \"HS_Cober_1\" \"HS_Cober_2\" \"HS_Cober_3\" \"HS_Cober_4\"\n",
       "[331] \"HS_Cober_5\" \"HS_Cober_6\" \"HS_Cober_7\" \"HS_Cober_8\" \"HS_Cober_9\"\n",
       "[336] \"HS_Registr\" \"HS_Usuario\" \"HS_Usuar_1\" \"HS_Total.d\" \"HS_Cober10\"\n",
       "[341] \"HS_Cober11\" \"Vulnerabil\" \"Threat\"     \"Lack_Respo\" \"INFORM\"    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PolygonShape <- readOGR(dsn = \"Data\\\\Colombia\", layer = \"GWR - Colombia\", drop_unsupported_fields=FALSE, disambiguateFIDs=TRUE)\n",
    "cat(\" \")\n",
    "cat(\"The final names of the variables that were imported are the following:\")\n",
    "\n",
    "colnames(PolygonShape@data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Model definition and selection of variables\n",
    "\n",
    "Note that if the user already possess a strong conceptual framework which suggest a model specification, this step can be ommited. \n",
    "\n",
    "Include in ListVariables all the variables to test in the model selection process, starting by the dependent variable. Make sure the names of the variables typed below match exactly with those presented above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ListVariables <- list(\"E_s11_to_1\", \"MurderRate\", \"ProxyGDP\", \"Poverty\", \"SISBEN1PC\", \"GC_indrura\", \"Vulnerabil\", \"Threat\", \"Lack_Respo\", \"TransEducP\", \"TransAlimE\", \"HS_Cober_7\", \"HS_Cober_1\", \"G_IGA_tota\", \"GC_discapi\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given that the calculations produced below require a complete database (with no missing values), all rows that contain missing values for the list of variables specified above are removed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Deleting rows: 24724925325469990894496299010201039104524224825025125225525625891495598510901100111511163569482225538912713615522723024427638145488589893410831099\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "PolygonShapeTrimmed <- PolygonShape[c(\"Lat\", \"Long\", \"admin2Pcod\", \"E_s11_to_1\", \"SISBEN1PC\", \"MurderRate\", \"ProxyGDP\", \"Poverty\", \"GC_indrura\", \"Vulnerabil\", \"Threat\", \"Lack_Respo\", \"TransEducP\", \"TransAlimE\", \"HS_Cober_7\", \"HS_Cober_1\", \"G_IGA_tota\", \"GC_discapi\")]\n",
    "PolygonShapeTrimmed <- sp.na.omit(PolygonShapeTrimmed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This produces Figure 8\n",
    "\n",
    "Using the variables defined as dependent and independent, the code proceeds to execute the pseudo-stepwise procedure outlined in Figure 1 (sections 3.1 through 3.4). The graph produced shows the iteration process, together with the result of the estimation (the longest line). If the user has a strong analytical framework that suggests a model specification, this step can be omitted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now calbrating the model: \n",
      " E_s11_to_1~MurderRate \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~ProxyGDP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Poverty \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~SISBEN1PC \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~GC_indrura \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Threat \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Lack_Respo \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~TransEducP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~TransAlimE \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~HS_Cober_7 \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~HS_Cober_1 \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~G_IGA_tota \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~GC_discapi \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+MurderRate \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+ProxyGDP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+Poverty \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+SISBEN1PC \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_indrura \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+Threat \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+Lack_Respo \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+TransEducP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+TransAlimE \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+HS_Cober_7 \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+HS_Cober_1 \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+G_IGA_tota \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+MurderRate \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+ProxyGDP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Poverty \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+SISBEN1PC \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+GC_indrura \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Threat \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+TransEducP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+TransAlimE \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+HS_Cober_7 \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+HS_Cober_1 \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+G_IGA_tota \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+MurderRate \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+ProxyGDP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Poverty \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+SISBEN1PC \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+GC_indrura \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+TransEducP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+TransAlimE \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+HS_Cober_7 \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+HS_Cober_1 \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+G_IGA_tota \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+MurderRate \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+ProxyGDP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+Poverty \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+GC_indrura \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+TransEducP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+TransAlimE \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+HS_Cober_7 \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+HS_Cober_1 \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+G_IGA_tota \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+MurderRate \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+ProxyGDP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+GC_indrura \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+TransEducP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+TransAlimE \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+HS_Cober_7 \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+HS_Cober_1 \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+G_IGA_tota \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+ProxyGDP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+GC_indrura \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+TransEducP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+TransAlimE \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+HS_Cober_7 \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+HS_Cober_1 \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+G_IGA_tota \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+ProxyGDP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+GC_indrura \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+TransEducP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+TransAlimE \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7 \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_1 \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+G_IGA_tota \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+ProxyGDP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+GC_indrura \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+TransEducP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+TransAlimE \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+HS_Cober_1 \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+G_IGA_tota \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+HS_Cober_1+ProxyGDP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+HS_Cober_1+GC_indrura \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+HS_Cober_1+TransEducP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+HS_Cober_1+TransAlimE \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+HS_Cober_1+G_IGA_tota \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+HS_Cober_1+TransEducP+ProxyGDP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+HS_Cober_1+TransEducP+GC_indrura \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+HS_Cober_1+TransEducP+TransAlimE \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+HS_Cober_1+TransEducP+G_IGA_tota \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+HS_Cober_1+TransEducP+GC_indrura+ProxyGDP \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+HS_Cober_1+TransEducP+GC_indrura+TransAlimE \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+HS_Cober_1+TransEducP+GC_indrura+G_IGA_tota \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+HS_Cober_1+TransEducP+GC_indrura+ProxyGDP+TransAlimE \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+HS_Cober_1+TransEducP+GC_indrura+ProxyGDP+G_IGA_tota \n",
      "Now calbrating the model: \n",
      " E_s11_to_1~Vulnerabil+GC_discapi+Lack_Respo+Threat+SISBEN1PC+Poverty+MurderRate+HS_Cober_7+HS_Cober_1+TransEducP+GC_indrura+ProxyGDP+G_IGA_tota+TransAlimE \n"
     ]
    },
    {
     "data": {
      "image/png": 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QMhEewkoKugI/feRyVBSCdMlxlZ6bGz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",
      "text/plain": [
       "Plot with title \"View of GWR model selection with different variables\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DeVar <- ListVariables[[1]] \n",
    "InDeVars <- unlist(ListVariables[2:length(ListVariables)], recursive = TRUE, use.names = FALSE)\n",
    "\n",
    "model.sel <- model.selection.gwr(DeVar ,InDeVars, data = PolygonShapeTrimmed, \n",
    "                                 kernel = \"bisquare\", adaptive = TRUE, bw = 80) \n",
    "sorted.models <- model.sort.gwr(model.sel, numVars = length(InDeVars), \n",
    "                                ruler.vector = model.sel[[2]][,2]) \n",
    "\n",
    "model.list <- sorted.models[[1]]\n",
    "\n",
    "model.view.gwr(DeVar, InDeVars, model.list = model.list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the procedure for model selection, the code plots the progression of the modified Akaike Information Criteria as the different models are used. This step can also be omitted if a strong theoretical model is already chosen.\n",
    "\n",
    "Note that if, when a large number of potential independent variables, the AIC curve flats out at a specific point, the user should check which were the variables that were included in the model before said brake point, and either manually modify the equation, or change the index of the sorted.models list to include only the wanted elements. For instance, if when including 50 variables the modified AIC graph flattens out after 15 variables, the model to be estimated in In [21], In [22], and In [27] should be changed from length(sorted.models[[1]]) to the list index that corresponds to the 15th variable chosen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This produces Figure 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AkHQ5AAAU4vyH48wZsg4WAcBumWECSE\nymXXrs5Vpj+0wYOuHd+SBFFuz5GuSl0fPgSJ7+2DLMeDDVWm8nr/IPFNshDmfNTurJKSICE0\n7oe/7+n7Y5iuHQ5mj+tIp/2DxGADZDFFCBBAkAABBAkQQJAAAQQJEECQAAEECRBAkAABBAkQ\nEG+QCCsEESRAAEECBBAkQABBAgQQJEAAQQIEECRAQLxBAgQRJEBAvEEirBBEkAABBAkQQJAA\nAfEGiSRBEEECBBAkQABBAgREHCRADkECBEQcJNIKOQQJEECQAAEECRBAkAABBAkQQJAAAQQJ\nEBBxkAA5BAkQEHGQSCvkECRAAEECBBAkQEDEQSJJkEOQAAEECRBAkAABMQcJEEOQAAExB4m4\nQgxBAgQQJEAAQQIEECRAAEECBBAkQABBAgTEHCRADEECBMQcJOIKMQQJEECQAAEECRBAkAAB\nBAkQQJAAAQQJEBBzkAAxBAkQEHWQyCukECRAAEECBBAkQABBAgQQJEAAQQIEECRAQNRBAqQQ\nJEBA1EEir5BCkAABBAkQQJAAAQQJEECQAAEECRBAkAABUQcJkEKQAAFRB4m8QgpBAgQQJEAA\nQQIERB0kkgQpToN0O+eqlRc3WyX+hyBBiMMg1amaZFZK/BdBghCHQSpUcr3rW1WZqMJGif8i\nSBDiMEiJuo+37yqxUQLYicMgKfXsF7ESwE7ibpEILIS4PUcqK32LcySExuXwd2aM2qW1lRL/\nRJAgxO11pEJfR0ryM9eREBZmNgACCBIggClCgACmCAEC4p4iBAiJ+4IsIOTXINVFm4ikeHlZ\nqH+cf1OECCyE/BikKtGJUCqp3j7OwxaJIEHIj0HK1Klti+pC5W8fxxQhhOvHII09tNddtQ5T\nhBCsH4OUqC4Q9SdB8m+KEEmCkB+DVKiszcQte91V+6WEVQQJMn4dtcs+usD60WpNP6/t06Ku\nCiFwP19Hura9tezyyQO7ofJz2ix/Fd+q7xAkyHB4QVYPldeJT1OEACEOg3RSed3871Q1mTr5\nMfwNCPkpSNVJd+nq9KOenWpH+FQ3zFf7cUGWxELIL0Fq+mr6Mmz50cSG7lJTooxfJLfqOwQJ\nMn4JUtpNa9Cj3+n7x53aKULnbp5Q/fokiSDhYH4IUqnO4325ejMO99Dz64r7I0+aJJWpKoW3\n6jsECTJ+CNJJTdN8qk8uJJXJdKHo/HJJgoSD+SFI/3hbxOB60u+Szc9vTqkIEg7mhyAl/w/S\nf0tYR5Ag46eu3XSeU37wNoovSgAH8UOQ7tOgd5V8MNhgrOFdVYKEg/ll+LtQybkdzL6fk/9N\nWiVICM1PMxvO4yDc6ZPHzQlvFbCn3+baVUX2ySBc55YQJITK5Set1rnKdOTo2iE0bj/7+6r0\noARBQmgcf4h+lbXvpSBICM1PMxu+eXf4WSWlN0Fy9552BM55kB739P3Cjo5vl58OgbA57tpp\nJ0+C5PZzVhC0PYLkRYkHQYIgoSDdi5dvHZcoYQE5ghSJIFXtJ2wdMUjDYANxwq9+DlJ9bQcP\nspdveP2xhG00TPjZj0G6dp+0+tEUoS9LWMepEn73S5DKU/uJ+MVd/igkSDiYn94h26So/Qj9\ngwdp6Np1T4NI4Rs/XZAthhtim7Mo4Uifo/YHjRO+Qos0lJxN0iBN+B+Bc6RbeEGiXcI/MWo3\n1py6drRL+C+h60j5ka8jDUXHwQbaJfxX3DMbntY32yXgvajn2r3YgKldooOHD8Q8+/sdBsTx\nMYL0gtku0TDhFYL0xtTBI0l4jiC9M+WIJOEpgvTWOIGIIOEpgvQhzpTwCkH6lDGzFVgiSP/B\npSU8QZD+Yz53iDhhRJD+xZw7RDcPE4L0P9NQ+Lybx9trI0eQvrBul+YXbclTfAjSN6YRPLXC\n0F6UCNL3lt08o40iSbEhSD9ad+0IUowI0q/Wgw2MjkeIIFnAJIj4ECRLtkbHES6CZMnG6Dhx\nChhBsmU1Ok5nL2QEyZono+MIEkGyy+jazc+aEBaCZJkx2MC8h4ARJIe4XBsuguTYGCTSFBSC\n5NqYI5IUEoLk3OxTiUhTIAjSPri0FBiCtBPecREWgrQXvvAiKARpV0zFCwVB2hdfEBgIgrS/\n+VQ80nRIBMkD5sAD7dIxESQfLD+ViPcDHg5B8sfqfInW6TgIkmfW7wfEERAk36w/FBkHQJC8\nRNfuaAiSnxhsOBiCBAggSIAAggQIIEgHwAmT/wiS/xjCOwCC5D0uKh0BQfIek8OPgCD5z+za\nzd4DSKj8QZAOYIoNc1p9RZAORa3QLvmBIB3LenL40C6Rp10RpINZfhz/1C6RpD0RpEPaPGvi\nnbU7IkhHNzVIfNPmjgjS4Q2NEN+0uSeCFIpn37RJmpwgSEFZj0LQLrlBkMKy/KZNpug5QpDC\nxTdtOkSQAkeO3CBIoSNHThAkQABBigPtkmUEKQqcKdlGkGLA2J11BCkGBMk6ghSF+TRWMiWP\nIMXBeBsTk8NtIEjxmL91ic6eKIIUj1mQOG2SRZAiYnbtCJIsghQTc7CBMyVRBClaxEkSQYra\nfOCBOH2PIEVt60MeuNj0DYIUt6efmcJQxP8QpMgtx/HmF5tolz5FkPDY+JAHrtr+E0HCaDWP\niItNHyNIWDAGGwjSxwgSXiBHn3IapNs513/i8uJmqwRkkaMPOQxSnRrDQpmVEsBOHAapUMn1\nrm9VZaIKGyWAnTgMUqLu4+27SmyUAHbiMEiz7vbrvjdB8gtnSm/RIuEtxu7ec3uOVFb6FudI\nh8LVpA+4HP7OjFG7tLZSAhYw7+4Dbq8jFfo6UpKfuY50JEOOSNJzzGzAe0wXeosg4TN08F5i\nihA+RAfvFaYI4VNmB8/4rlq0mCKE/5i94W9oncgTF2TxT+t3pdPbazFFCP8zdfAWH+8QN1ok\nfMFsihjOazFFCN8wT47o4D2YIgQBXK9lihCEECQXD/GwBITFnSOPgmSOBMX8ihxW3K/aLkF6\nu8ujfklwRAQJEOD0guzHvTeCdFixdvAcBumWEKTgRXuC67JrV+cq01dk6dqFKt6hIrfnSFel\nrg+CFC6CZPchoypTeU2QwtXnKL4wOR+1O6ukJEjhivV7M90Pf9/T93s5tlchMDF28Pa4jnQi\nSGEjSLYe4mEJWBRfjpjZABuiyxFBAiQwRQgQwBQhQABThAABTBGCNTENOTBFCLZENV2IKUKw\nxPj8yL03xQGmCMGSuD6DgylCsGVskGLo4TFFCNaYM8FDjxNBgmVjjoI+YSJIcCH4EyaCBCdm\nJ0wBhokgwQ3jTCnE8yWCBJeML64Iq5dHkOBakOdLBAnOzS8whYEgwb0ArywRJOwlqE/uIkjY\nUzAdPIKEPREkq0LYs/hIIDkiSNhZl6PDp4kgwQPHb5cIEvYXwJkSQcL+CJIlx96n+LfD54gg\nwQuHH3IgSPDG0C4dMU8ECb6Y3vZ3wCQRJPjCeJ/S8ZJEkOCNqUEiSDIOtxshYjhFOl6OCBL8\nc8AcESRAAkGCp47VLhEk+Olg70QnSPDS0T6ziyDBS0f7zC6CBD8d7OIsQYKnjvUhQwQJXjtI\njggSjsD/OBEk+O8AHTyCBO8d4WuVCBK8d4RrSgQJ/jvAUDhBwgH4/z4lgoSj8DlHBAnH4fNH\noxAkHIun7RJBwqH4eqZEkHAoBMmzEjgoP3NEkHA0XuaIIOGgPMsTQcIh+dbDI0g4Iu/msRIk\nHJF381gJEg7Jt3msBAnH5Nk8VoKEA/MmRwQJh2bOY901UwQJh+fDF/0RJBydFx/KSpBwdARp\nzxIIB127HUsgIAw27FcCkESQEJad2iWChKDsdaZEkBCS3cbuCBJCQpBcl0CY6No5LoFAMdjg\ntgQgiSAhRM7bJYKEALk/UyJICM8OY3cECeEhSO5KIGR07ZyVQNBm88FdhIogIVh9u+SkeSJI\nCJXxIZL2k0SQECqCRJAgga4dQYIEBhvsl0Bs7MaJICEOYz/v8bARKoKEKMy+B2YWKqH1O3mI\nhyUQF7UiOwhBkBAHYyjcxkezEiREwhgKt3B9iSAhKsZgA107QAKDDYAYmau2BAlxE5pHRJAQ\nNamZrQQJUSNIgAS6doAEBhsAbxAkQABBAgQ4DdLtnOvBkby42SoB7MJhkOrUmHSbWSkB/Obr\nIQeHQSpUcr3rW1WZqMJGCeAn3w+COwxSou7j7btKbJQAfvHDZVmHQZpt4evNJUjYwzGCRIsE\n3x2ia9ecI5WVvsU5Ejx1hMGGR2aM2qW1lRLAPtxeRyr0daQkP3MdCWFhZgMggCABApgiBCx9\nMeTAFCFg4ZtPNGaKEDD31Scac0EWmPvqE42ZIgQsfPNxKLRIwNIXH4fCFCFgm6+DDUwRQriY\nIgQIYGYDIMCfIKmvRh0BL7gMUn1SKiv7lTD8jZC4nCKUdBPtupUQJITE6fD3pUnTJdHT7AgS\njuHD8wynF2T1jypJK4KEg/j0jH2HKUJ1lhEkHMPHY18Og5Sq4SJsmhEkHIKPQbqoU3+rUhlB\nwiF42LV7FOMmlW+2jiDBF/4NNjwe93y4VZ0IEkLiz8wGxyWAf3jbLhEk4K33Z0q7BMlGvAFr\nPhi7I0jAO34FSc3ZKAFY4VXX7pYQJByUV4MNda4y/aENdO0QGrfnSFelrg+ChPA4HmyoMpXX\nBAnBcT5qd1ZJSZAQGvfD3/f0/TRAgoSD2eM60okgITRMEQI+9aIFIEjAh15d/iRIwGdeTiQg\nSMBnCBIgga4dIIHBBsAuggQIIEiAAIIECCBIgACCBAggSIAAggQIIEiAAIIECCBIgACCBAgg\nSIAAggQIIEiAAIIECCBIgACCBAggSIAAggQI8DRIwMF8cZTLB8fbqhGXjvRpOyxNkKIoHenT\nJkiUDqd2HKUJUhSlI33aBInS4dSOozRBiqJ0pE+bIFE6nNpxlCZIUZSO9GkTJEqHUzuO0gQp\nitKRPm2CROlwasdRmiBFUTrSpx18kIDAECRAAEECBBAkQABBAgQQJEAAQQIEECRAAEECBBAk\nQABBAgQQJEAAQQIEECRAAEECBBAkQMAOQSoSlRS166qXdKy6wwbc+v3svPT9pNSp2qN2bdRz\nWfoyHNJu67sPUqY/7j91XLXQVZN6nw2ok24/Oy9d7va0q6QrXTkufR++SsIo6qK+8yDdVHJ/\n3BN1c1r1rk51+8fqtM8G5N2r67500hSsc1W4r31qizZ/vxzv8aZMd0gbRZ3Udx6kQpXN/6/q\n7LRq3j3PdifvsAHX/ht3nJe+6qO5Von72mqXPX5RWV/YKOqkvvMg5apt7O8qd1241e5k9xtQ\nDa+u89IndR9uuq7dd2bbDDss3fzh6INkFHVS33mQjL9UztUq22MDMlV11ZyXTtXjnOherfPa\n575rd3Za+r6s1v5wUj+qIF3aNt75BpzV9bFTkJTK9Rn/HrUv7WhDcnFemiBZVyX5DhuguxS7\nBakdbDg5bhY6Zz1Udn4QJEsFdwtSnWR7bEDaDj7vFqT2HKlqR35d1760XbsmwxeCZEeyW5Cy\ndI8NOOkho66a8+duHEKua6eqPTOr2wy7Ld2XSRw/9Z1G7Srno3ZVmlV7bID5lfPOn7sx6u+6\nttqr9GzUrppG7SzXdx6ks/4LXeoxHYdKle2zAWaQnD/3rmDVPnfXtbtmQF/Cclu6D5JR1En9\nSGY2VGOO9tmAnWY2NGdHdXuicnVfu1Dt5LbC/aSKWGY2NJ3nVvZ+QUmnqVnYZQP6V9d56fNU\n0HXtbKfSw6lQ6rb+DoNneiqu46JG/2qXDehfXfely2wo6Lz2VM9p6SFItdv6e1wXBYJDkAAB\nBAkQQJAAAQQJEECQAAEECRBAkAABBAkQQJAAAQQJEECQAAEECRBAkAABBAkQQJAAAQQJEECQ\nAAEECRBAkAABBAkQQJAAAQQJEECQAAEECRBAkAABBAkQQJAAAQQJEECQAAEECRBAkAABBAkQ\nQJB2leSX7rvWq0uebC6x+FL7/33H/f+Wxg/Y07tSSp30jZN6ctATpGNgT+9KqbRriJKUIB0a\ne3pXShXq3vy8Nz8J0pGxp3elVKkuzc+LuvYH/SVV6aX7xyJRRZ+F5t7k0j1gemiVq+Q83tf+\nv/nvrO9rYll09xXj93kb66hTlbt5gtEgSLtqjml9SOeq6uKQqVY23sz13fl4rxmkpL3zPA/S\nub2v1A8t2t/zcXXmOvIuZ5BDkHbVHP1p+xKopIvDVSX3xz1R1+lmc3epsvpRZ6qcB6m587vj\nZ7sAAAGcSURBVKLSeZD0fd3/Ex22YXWzdTQ3IYsg7Ur3vW6Pmzo9+qanOdDbg769edM3VXuz\nPfB122UG6fYYO3Tjre6+avy9W12+WMfN+RMNHkHaVXO0X5ve2blpM3Qc+pxM8ehv9uZBmi85\nv/XZOiCGfbqr5piumuYna9oQgnRo7NNdtcd0ourufObxPEizB5i3/hGkjXVADPt0V+0xfVJF\nO71hdo6UDzdv3TlSaT7AvDVm5LYdpNt8dYt1QAz7dFftMX1t+lvXx2rUrpxG7fS9j8tysGH4\nf6ou7XjcZpC6dZRP1gEx7NNdtcd0pfQw2+o6kr7wc9J3d/cm1XaQLuMFp3WQTvrfHk/WATHs\n013pYzppT5GGw/uSjDMbzrOZDepUPbaD1C54enaOVHSzH7bXATHsU0AAQQIEECRAAEECBBAk\nQABBAgQQJEAAQQIEECRAAEECBBAkQABBAgQQJEAAQQIEECRAAEECBBAkQABBAgQQJEAAQQIE\nECRAAEECBBAkQABBAgQQJEAAQQIEECRAAEECBBAkQABBAgQQJEDAH3mqy4Vnjd2tAAAAAElF\nTkSuQmCC",
      "text/plain": [
       "Plot with title \"Alternative view of GWR model selection procedure\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(sorted.models[[2]][,2], col = \"black\", pch = 20, lty = 5, \n",
    "     main = \"Alternative view of GWR model selection procedure\", \n",
    "     ylab = \"AICc\", xlab = \"Model number\", type = \"b\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Ordinary Least Squares\n",
    "\n",
    "### This produces Table 3 and the variable Residuals for the creation of Figure 5\n",
    "\n",
    "- The CSV ModelOLS contains a variable called Residuals. This variable will be used for the creation of Figure 5. The detailed instructions are included at the end of this code.\n",
    "\n",
    "An initial OLS regression is performed with all the variables specified on the ListVariables list. Remember that the first element of said list should be the dependent variable, while the rest should be independent variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = as.formula(sorted.models[[1]][[length(sorted.models[[1]])]][[1]]))\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-7.9422 -1.6557 -0.0638  1.4837 10.8037 \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  4.564e+01  9.593e-01  47.582  < 2e-16 ***\n",
       "Vulnerabil  -6.474e-01  8.082e-02  -8.010 3.01e-15 ***\n",
       "GC_discapi   2.757e-04  1.494e-03   0.185   0.8536    \n",
       "Lack_Respo  -5.360e-02  7.173e-02  -0.747   0.4551    \n",
       "Threat       1.207e-01  5.724e-02   2.108   0.0353 *  \n",
       "SISBEN1PC   -1.773e+01  3.871e+00  -4.580 5.20e-06 ***\n",
       "Poverty      4.197e-02  7.559e-03   5.552 3.56e-08 ***\n",
       "MurderRate  -1.156e-02  2.808e-03  -4.116 4.15e-05 ***\n",
       "HS_Cober_7   5.764e-03  3.870e-03   1.489   0.1367    \n",
       "HS_Cober_1  -5.864e-03  3.409e-03  -1.720   0.0857 .  \n",
       "TransEducP   5.273e-07  1.091e-06   0.483   0.6289    \n",
       "GC_indrura   1.367e+00  4.869e-01   2.806   0.0051 ** \n",
       "ProxyGDP     3.229e-01  1.293e-01   2.498   0.0127 *  \n",
       "G_IGA_tota   6.908e-02  9.178e-03   7.527 1.11e-13 ***\n",
       "TransAlimE  -2.082e-04  4.513e-05  -4.613 4.46e-06 ***\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 2.458 on 1055 degrees of freedom\n",
       "Multiple R-squared:  0.4969,\tAdjusted R-squared:  0.4902 \n",
       "F-statistic: 74.41 on 14 and 1055 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "attach(PolygonShapeTrimmed@data)\n",
    "modelOLS <- lm(as.formula(sorted.models[[1]][[length(sorted.models[[1]])]][[1]]))\n",
    "summary(modelOLS)\n",
    "tidy_modelOLS <- tidy(modelOLS)\n",
    "write.csv(tidy_modelOLS, \"Tables\\\\ModelOLS.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Calculating Moran's I\n",
    "\n",
    "A test for spatial autocorrelation, Moran's I, is performed. Here, the null hypothesis is that there is no spatial autocorrelation. A p-value under 0.05 will reject this hypothesis with a 95% of statistical confidence, in favor of the alternative hypothesis of the existance of spatial autocorrelation for the dependent variable. If this is the case, this means that the user should use GWR instead of OLS, since using OLS in the presence of spatial autocorrelation can lead to bias in the estimation of regression coefficients. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$observed</dt>\n",
       "\t\t<dd>0.140936788579202</dd>\n",
       "\t<dt>$expected</dt>\n",
       "\t\t<dd>-0.000935453695042095</dd>\n",
       "\t<dt>$sd</dt>\n",
       "\t\t<dd>0.0017834383152867</dd>\n",
       "\t<dt>$p.value</dt>\n",
       "\t\t<dd>0</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$observed] 0.140936788579202\n",
       "\\item[\\$expected] -0.000935453695042095\n",
       "\\item[\\$sd] 0.0017834383152867\n",
       "\\item[\\$p.value] 0\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$observed\n",
       ":   0.140936788579202\n",
       "$expected\n",
       ":   -0.000935453695042095\n",
       "$sd\n",
       ":   0.0017834383152867\n",
       "$p.value\n",
       ":   0\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$observed\n",
       "[1] 0.1409368\n",
       "\n",
       "$expected\n",
       "[1] -0.0009354537\n",
       "\n",
       "$sd\n",
       "[1] 0.001783438\n",
       "\n",
       "$p.value\n",
       "[1] 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DependentVariable <- c(ListVariables[[1]])\n",
    "# MoransIListVariables <- c(ListVariables[[1]])\n",
    "# MoransIListVariables <- append(MoransIListVariables, \"Lat\")\n",
    "# MoransIListVariables <- append(MoransIListVariables, \"Long\")\n",
    "PolygonShapeData <- PolygonShapeTrimmed@data\n",
    "PolygonShapeData.dists <- as.matrix(dist(cbind(PolygonShapeData$Long, PolygonShapeData$Lat)))\n",
    "PolygonShapeData.dists.inv <- 1/PolygonShapeData.dists\n",
    "diag(PolygonShapeData.dists.inv) <- 0\n",
    "Moran.I(PolygonShapeData$E_s11_to_1, PolygonShapeData.dists.inv, na.rm=FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtaining summary statistics\n",
    "\n",
    "This code computes a series of geographically weigthed summary statistics (GWSS), using different kernels for the conformation of the proximity matrix. In particular, the one below uses a boxcar distribution, which establishes a weight of 1 for observations where the distribution is within the radius defined by the bandwidth, and 0 otherwise. For more information, search help(gwss)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gw.ss.bx <- gwss(PolygonShapeTrimmed, PolygonShapeTrimmed, vars = c(\"E_s11_to_1\", \"MurderRate\", \"ProxyGDP\", \"Poverty\"), \n",
    "#                  kernel = \"boxcar\", adaptive = TRUE, bw = 48, quantile = TRUE)\n",
    "gw.ss.bx <- gwss(PolygonShapeTrimmed, PolygonShapeTrimmed, vars = c(ListVariables), \n",
    "                 kernel = \"boxcar\", adaptive = TRUE, bw = 48, quantile = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same summary statistics are obtained, now using a bi-square distribution, where the weigth of each surrounding observation is equal to (1-(vdist/bw)^2)^2 if the distance to the observation (vdist) is smaller than the bandwidth (bw), and 0 otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gw.ss.bs <- gwss(PolygonShapeTrimmed, PolygonShapeTrimmed, vars = c(\"E_s11_to_1\", \"MurderRate\", \"ProxyGDP\", \"Poverty\"), \n",
    "#                  kernel = \"bisquare\", adaptive = TRUE, bw = 48)\n",
    "gw.ss.bs <- gwss(PolygonShapeTrimmed, PolygonShapeTrimmed, vars = c(ListVariables), \n",
    "                 kernel = \"bisquare\", adaptive = TRUE, bw = 48)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The basic map layout is defined for the visualization of the GWSS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "map.na = list(\"SpatialPolygonsRescale\", layout.north.arrow(),\n",
    "              offset = c(329000,261500), scale = 4000, col=1)\n",
    "map.scale.1 = list(\"SpatialPolygonsRescale\", layout.scale.bar(),\n",
    "                   offset = c(326500,217000), scale = 5000, col=1, fill=c(\"transparent\",\"blue\"))\n",
    "map.scale.2  = list(\"sp.text\", c(326500,217900), \"0\", cex=0.9, col=1)\n",
    "map.scale.3  = list(\"sp.text\", c(331500,217900), \"5km\", cex=0.9, col=1)\n",
    "map.layout <- list(map.na,map.scale.1,map.scale.2,map.scale.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Three main palettes are chosen, which can be costumized and adjusted as needed. More colors can be chosen from the options presented by inputing display.brewer.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mypalette.1 <- brewer.pal(9, \"Reds\") \n",
    "mypalette.2 <- brewer.pal(9, \"Blues\") \n",
    "mypalette.3 <- brewer.pal(9, \"Greens\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preformatted variables are created for a number of variables selected. This can be further expanded if more variables want to be plotted. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "LSD_DepVar <- paste(ListVariables[[1]],\"LSD\", sep=\"_\")\n",
    "IQR_DepVar <- paste(ListVariables[[1]],\"IQR\", sep=\"_\")\n",
    "CorrDepVarIndVar1 <- paste(\"Corr\",paste(ListVariables[[1]], ListVariables[[2]], sep=\".\"), sep=\"_\")\n",
    "CorrDepVarIndVar2 <- paste(\"Corr\",paste(ListVariables[[1]], ListVariables[[3]], sep=\".\"), sep=\"_\")\n",
    "SpeRhoDepVarIndVar1 <- paste(\"Spearman_rho\", paste(ListVariables[[1]], ListVariables[[2]], sep=\".\"), sep=\"_\")\n",
    "SpeRhoDepVarIndVar3 <- paste(\"Spearman_rho\", paste(ListVariables[[1]], ListVariables[[4]], sep=\".\"), sep=\"_\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A map is constructed by using the shapefile already loaded into the program, and by using the geographically weigthed standard deviations of the dependent variable. An example using the actual name of the variable, as well as a more evident title for the graph, is presented as a comment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a5EURq//dsglam68l0VJEwwtEHCoVnCQ6ly4iciKUA6V3fmqJJvKKMyWgqSPavs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\n5+B6gT0ESMfpnsMoscO+6IOn9Ckj9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B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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# spplot(gw.ss.bx$SDF, \"E_s11_to_1_LSD\", key.space = \"right\", \n",
    "#        col.regions = mypalette.1, cuts = 7, \n",
    "#        main = \"GW standard deviations for SABER11 (basic)\", sp.layout = map.layout)\n",
    "spplot(gw.ss.bx$SDF, LSD_DepVar, key.space = \"right\", \n",
    "       col.regions = mypalette.1, pretty=TRUE, cuts=8,\n",
    "       main = \"GW standard deviations for dependent variable (basic)\", sp.layout = map.layout)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same map is created for the robust geographically weigthed inter-quartile ranges for the dependent variable, with its corresponding example. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Lp9xDcYMtmYbwcjGgUhqS5j1KxA7VuKEN7K3Y7RCZQg9bWAezzV4hlXjvpHljHp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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# spplot(gw.ss.bx$SDF, \"E_s11_to_1_IQR\", key.space = \"right\", \n",
    "#        col.regions = mypalette.1, cuts = 7, \n",
    "#        main = \"GW inter-quartile ranges for SABER11 (robust)\",\n",
    "#        sp.layout = map.layout)\n",
    "spplot(gw.ss.bx$SDF, IQR_DepVar, key.space = \"right\", \n",
    "       col.regions = mypalette.1, pretty=TRUE, cuts=8,\n",
    "       main = \"GW inter-quartile ranges for dependent variable (robust)\",\n",
    "       sp.layout = map.layout)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A map of GW correlations is shown between the dependent and the first independent variable, constructed using the box-car kernel described above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Lbau55tUCeAzScCKUBKKT3TyyqB3Tcot0t5cYjuJPftxjV9ZZcUIJ1fscZyV5O2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\nhYTsIP8/ampeuLsAjMUAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# spplot(gw.ss.bx$SDF, \"Corr_E_s11_to_1.MurderRate\", key.space = \"right\", \n",
    "#        col.regions = mypalette.2, at = c(-0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1), \n",
    "#        main = \"GW correlations: SABER11 and MurderRate (box-car kernel)\", \n",
    "#        sp.layout = map.layout)\n",
    "spplot(gw.ss.bx$SDF, CorrDepVarIndVar1, key.space = \"right\", \n",
    "       col.regions = mypalette.2, pretty=TRUE, cuts=8,\n",
    "       main = \"GW correlations: Dependent and first independent variable (box-car kernel)\", \n",
    "       sp.layout = map.layout)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4b3itFhUGiohRpehywhBBBqbcL1h0Ze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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# spplot(gw.ss.bx$SDF, \"Corr_E_s11_to_1.ProxyGDP\", key.space = \"right\", \n",
    "#        col.regions = mypalette.2, at = c(-0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1), \n",
    "#        main = \"GW correlations: SABER11 and GDP (box-car kernel)\", \n",
    "#        sp.layout = map.layout)\n",
    "spplot(gw.ss.bx$SDF, CorrDepVarIndVar2, key.space = \"right\", \n",
    "       col.regions = mypalette.2, pretty=TRUE, cuts=8,\n",
    "       main = \"GW correlations: Dependent and second independent variable (box-car kernel)\", \n",
    "       sp.layout = map.layout)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same thing is done using the second independent variable. This can be easily replicated into any type of correlation, be it with more independent variables and the dependent variable, or even between two independent variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R3G/78pSLIDH4qThV/YJS0EqboXwYcA6YW6IJbDH/1qG+lufZAZcMHj+zDlUII0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\nhOwgAVJIyA4SIIWE7CD/DzuzkkQL7TvSAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# spplot(gw.ss.bs$SDF, \"Spearman_rho_E_s11_to_1.MurderRate\",key.space = \"right\", \n",
    "#        col.regions = mypalette.3, at = c(-0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1), \n",
    "#        main = \"GW correlations: SABER11 and Murder rate (robust)\", \n",
    "#        sp.layout = map.layout)\n",
    "spplot(gw.ss.bs$SDF, SpeRhoDepVarIndVar1,key.space = \"right\", \n",
    "       col.regions = mypalette.3, pretty=TRUE, cuts=8, \n",
    "       main = \"GW correlations: Dependent and first independent variable (robust)\", \n",
    "       sp.layout = map.layout)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the same is presented between the dependent and the third independent variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UR0+oqoxpHku3Pv6Gs6pACpN8WPiCTk\nyA0VGZAsY+/12C45IJ1MkfMMLgXR5VXaJwFSZ4ofEekceZRMmGfCuQZIiBB6KBPcfZ1BpI8A\nqTvF+yWTI15pDqTqZ+8Qzg1eEliQ+v5SGAVI3SneK4wOg0KGEODIlwhhFkLXyU8JBkPSwO9k\nps7+xq+VAKkzxTuFvRFAUQMpbxX5BiiHIBp/iukfCUpQIfS1/FGA1J3iPaIpb+LBWDdCRBYk\n10sibiRBpFIZk/16+bokAVJnirdLMXhEyV9UMwrk38B1hDxHDiQsvguQAqRrK94sxG5I/JD0\nkqqdpCLYI2xqFqRB+039XJ+DJEDqTPFmwbiOWUpoKSqK2rsHyboWh9LMbKSzvu5ZEiB1pniz\nkOsApYSdpPB4eJb5YugEsaI14MinG3LK4Qs6pACpN8WbBUECZuxgkmbyJB3xXptncd8BQRqp\n8EZfMff9ESB1p3iTUFW0q1R0kiTKq2KU5qHbXpJ4JRxF+nqRXYDUm+Itcg/DxAvlmgb1P9XS\nVNizMevPZ+7uKgYpvssovf67ni0BUmeK10seOdUxIegr+aydDsEydM3ZsyVIqa9EWh4UIAVI\nl1e8WiaOTAxnc9wOpFHCvubaKBmxEqRR12lINL38y54vAVJnilcLV/LAKKztEeE7ibr7/imk\nq/ujsQmSZhu+ZAfpI0DqTvFKIXZIMACrMZ5J0b1jvMYk1RtsgGSqwr8mRwFSb4rXSR7t8Ulu\n0x8iifFwcKgOEnGparOPxIs2fMHM9yQBUmeKVwqWaGsiAWdQ6LykAoqyNZJPq1k7krT3Fw3s\nAqTuFK+UFNiNFiSZRMFv1fo7FiVyrFVAShk74mTDiV/6TAmQOlO8UnLy4P4P5hRcyq6egRuF\nBotR9mYV7kYtu7v6hXmWBEidKV4pANIoKOFsJHZQFZKmA6ZGXInqe4U+3kdm9J39zU+SvSCZ\nWPi8wDhAqgt5kNQNSb1da1Qo5/s+ckcLiIFUeRWkr9tD2g0S4aHmxWslQKoJ575T2EXTGKsk\n7STbUEKhLkkitgIj75IojeWOqaN06cvyVNkHEuGx5Bp6pQRINdFBpGnwJ7+l4RwsfNIEyXgo\nxGhqAj58J0hqXPqyPFUOA+kcCZAqkvkhoeDD1IGb6qAGSDbB4BcyVpA4BSgvz/7up8khIEUf\n6VKSPBEJCdN7OYcwQq7Be5fRwMPeKMVvdY5GyWLwUWd/99MEQPr/2uI9twUp+kjXEgdSeo/7\nS9Jv0jHakqSUOFDHRGYqoEdOB6aufFWeLCtBqh4VfaRrChdws6lP7+n4LKflmvkGngsBvaUC\nIXBIGCie/dXPkwCpM8UrhLBLdE82cKeJ0wIYlVVIkrHVEXpKhdvSNgKkjwCpO8UrhCM7IcZJ\nBqNJEjywZQ1HAdJdAqTOFC8KopLNHWDAnpKkwcmwApMi+J0qRgESyj6QYkD2wiYj4RsRUADe\naeSPObOdx1MLh6TAzHHUWgPva8lOkCTjTfji9RIgFTKlFwgjO/2jaWrSt5JoJCd13EDMHEf4\nONqzv/x5sheki0iA5IWkSo4sBYyWTAvnWI+ntuZEnXCFyMz5I92I0G7aCpB6ULwgBHUMGNNp\n8ZyN3MyTKxNJeY26BZC8P0rDSWd//fMkQOpM8bwwR5A8cCkDeF4EcKQk8YJArmS1yRGucRyh\n3bQVIPWgeF6EowoFnM2rgzRgjHcHaRVHPG2dC2LP/v7nSYDUmeIVQmPpTmQQiZ1ORgVAGkDs\nyFGrokElsnYBUmeKV4jOB+cMHcmqwpppKDzSYElqOyQiXKqVIvt9lwCpM8VrpEh860qo1uHg\nU1nqIEHWAt0R+KIYjp0kQOpM8RrRWiCGwaOCIBHl/zxJ6rbcnFgp9jYzMgKkvBUg9aB4jfCq\nDGL64ofQJ0mGTjdb3SSzib0ilC8d2QVIvSleIcVDIzTHDYEcr1YiTzVSgPIn1XQDyQqtARJK\ngNSZ4hWSU9HgRLwfIvOW2ch5vdaQLFFwVJUAqTPFK6QAyQRsNsTTkSMa5AFH/FbFJYVDakiA\n1JniFeJWIyZeThjiugpIgz69EvyXA4kqj1aKVMNdAqTOFC8LGY+ka2VVQdLIzvaU+NMaSA2H\ndOFL8gIJkDpTvCjmGZaAEFlmBpuus39KkgSkOkfNxyp9GQmQOlO8KDiwk/PggyXHFHw7H+Wz\n4H5sl2ocUfOps19HAqTOFC+KWxT1/o8lQ/LbtVivHE7CwSNd+NiCFL2kAKk3xYtC3sSpyC2U\n4686ilSAlMlLh9nALtcISdHd2V/9TAmQOlO8KFWQoHdkylcLT1R8gDvbkgaXwfvaHAVIvSle\nEipAetdRWEgutECqkQRMaXu+TOiyV+Q1EiB1pnhBvHUX/qOencMpFiaqMyAJQLV+0tnf/GQJ\nkDpTPC+eo8LizTzYqtuBkiGb5cudpBpFAVKA1JvieaFKZFcDaSZ8g6FZcthVR2KDo0kCpM4U\nL4pJqxVeiRSTJkhQcmdAKhbgCpBUAqTOFC8LPm353ScFIJvdAmmQ1ISbqyRPnx1LlC58PV4j\nAVJnileI5NSmP+m1CfZMx4fZsS/cG0AYLyOJcH713PdHgNSd4hVi1vjRd9jo80bJESTuykFc\nuzc/xI+Mmq8sAVJnitdI7hrRD7Zx8UlAlIvlNIhjjoQngmxDFvfosUtfjddIgNSZ4jUC/SPp\nvbhngpn1uXTb8ITCH95uN5igITOezv2+V5AAqTPFK0RKdxCkDzMGBIUONvPAvaPETBYexc0v\nRisR2N0lQOpM8QoR3zO5JDlTTOBhWDb4RINCIyBluKbN0YN05ne9jARInSleIeqQfvww7sKk\n2sxM9IIjA9ItEVQHKfzRJAFSZ4qXRSY7ZJCUJVO6rSCYjFzuHN2cR7LuySyLcuqXvYwESJ0p\nXhYtZ5hA+pFpyqk7TVtzZGdT21IcZMApSJoaSP+f/X2vIQFSZ4orYm0ZkgrJ3n/8yCyxfzJ9\npGKIiFN0gM0U21n5+Sal/8/61heTAKkzxaVgljqXrcqo0Y/skyaG0h+hKQ8H+cGkQdZKycDA\nn0ICJJYAqTPFpUzr3OcMHUjm6Cc74pDYNfF6Xex9YOqRrGzHyQVNN7jgLm2d/eUvIwFSZ4oL\noQzSO4/CQuVO9kGTO1KQNN1AOCTLXSRJ22HfiAxH/Eb4I5EAqTPFToifqaydIi0F0kzDjwwS\nERQJ4TPO2Q0NkP42GYab254gu8pVuIAESJ0pRiHBCEGCWqCcX/hB0jX6gSlwSX5DnQNp+tum\n6opNig4SSIDUmWIQLHrTHtJ7kba748R5cA9SXhk895HStvaEfDzn3g2QVAKkzhSDiD+SJRy5\nh6RrNlLuI/3gHtIPsx7KxKCu1sWFQImSN7LBXeo33aocfXnvFCB1phiEQZISBZy3pyD9AJB+\nAEgJQJkQIdWqjMnbTynHYdFJ4ZkESLIVIPWgWM9gyl/DA14ZJH3mOHG2TjjCbAOBNxsGWZ/h\nBiA1ekmFQwqQAqTeFMsJZA7wsXo6dwK7R4Qg2fT3SFJtl7tJ0BWyDqkAScnhNMWZl+N0CZA6\nUywnwOk6MzEodZQkAU5cFyRDsURuHIk0BT4hgYHdHEjIUe5cnXk5TpcAqTPFcgLjSJW5QQQM\nZb8khXY5d2fmJOV0XULpZ6s3CxL2imogiR/LJH5dCZA6UywnUMFIxmXl32nlBk7XueAOGSpc\nUgLJdJOqFeAm3Xf2NTlTAqTOFMsJlBhpfQMOzkJ5g/aTph2Ksu8PohuPxb4JSUoPMORrxgeS\nVVZOvzSnSIDUmWLWXwXpXUEyNXeEIPGgrH22eW70lkeL3t4qLunG47Huseg8pnsPOL8qSQFS\nZ4qz+kpkp2kE8UqeJVP+jS6J17/ThF2FJJKyhurqkfwU9K9JUoDUmeKknfIyjVAiBBV30k3C\ndIPk7RQznIuU250jCVIP5DiiFN3x+Zx6cc6RAKkzxZNymXsHSzVOKGlk967CI0ma+n5PSXLO\nuGk4BiRhN4k4oitAsmNQTPaZF+ckCZA6UzwpxwwBd/I1+U1AzDuOJrmIDwK/mwGJk3LokXg/\nMiDpOpMYbF7WXJ4oAVJniifltmeT3hPPhONI0E1yj1HWueWAzgcGceKTyOTs0scmrpu2oMbi\nsubyRAmQOlP8IZGdA0kdUX7SuA/kUOCJR4rSW8JKnBG7JQTJZr917oVNe5x3bU6TAKkzxR9E\nmCPwLEHYpiTZSM9kGaZXiEhKfQtVOgHJlAsZkAYP0lckKUDqTLGAJDkG6d6AC8Kg7r14Aiai\n+K4+yZQskIJ0a4MkMAVIuhUgdaGYzINdSftGUtnwbrtD9qVxSPw4y8riDLJowyxIxP8HSLoV\nIF1e8UQRZpzZMeWJRe81kKD3VII05BElhacACd9wIA3YR/ra6xgHSD0pRnaGAUM8rLUzsRxT\nZNhCkNQlkaa3gRvwSFQUNkgfSc4hQAqQLq8Y8gwa3GWYaiCZtDeuejJgM+CRyBfWAUnEnLms\nHayaEiBNWwHStRWTLoPqOGKUJIJTdCANDkk8dySZfhAmGm5IkrImvhCHkoYAKW0FSJdWDGYL\nXSTLw4d9wiVWsQJJxZGDgAO5OrLsYLaBEx2Kc+GRLmsxz5IAqRfFNGCigb2AAWLajTxBkGjg\nbLcjcPqTfQ8GcrIAl3FPmufQk/Ae6eu5pACpD8WkBmzrcxxHHx+YWfCjSBkkG9XJNnod9UIM\nEq4GLiCBQwqQeCtAuqpiIQijuxZI5bNi3fCSGr8JEG2um9gLpRQDIWekoR10lr54bBcgXV8x\noGMt34V2sruvV7UpcKyMMKk/zNDdoNOUYILojg8YoLP01V1SgHR9xRjLYdrOciSzXI0PEgeR\nM+Ek2W8HgYIkeW4J8WStYjKjSAwSgB4gBUiXVSx9e+wgVUAaGCTMNpjCHdNJqoJkph2pX4Lu\n0g1DS3dKAdK0FSCdo3i2O5H7RWD42F9aBslEW7oKaxUkFFe9yu9qYCfnZVoIkAKkkxTf7bP9\nCSYGwNyL/hF/lA7DoSMESfBiEBwGhikJ7XjblAfh1Fg4gwApQDpJ8VRwWn3flgO1XzmSTKLO\ngySLnkBHp2zWrk5M5iX215xDsiB9sbxdgHS2Yqrmisvqg2WO7h99mOVQqJjfkJeGrOb+FK6b\nFbPGqgIIRQ35tBy2h12jDiRAOlcx5VVLire9rzEcNUmi4tljDiROisMhyyDdSpAIHts8EI9J\nOWUHXaMuJEA6VzFVba6SSDDZujZIfk1wZ9zQSSpbngHJ19npHHPus1UWfv1SJAVI5yqug/RR\nOp0i61wHiWfZbQdJVRaPFDMeSTtGpuCPao/HuKzdHC8B0qmKdcC0/KjVTaKZTlLyFMaWLVZF\n+TfgY6ru6h4J0oh+gJjGMrj7QiQFSKcqFturJu5KSCSiaoDEa8wZeGBt4ylxZzniB41pnwfz\ndaRDspK/EDUuKW+fH9j6gfikEiCdrZhniS9m7gZnyE2Qih6LzETPS4SbJjNIVOMo0yQrRt4X\n6vr5HwzeCoNp6e/KI5sOu1DXlgDpdMWy9HzRZllNx7Y7D1KRic7t80vjj9Lrsn+k47CTO3pL\nEE1/pnREztZxEpHTj5SflM76vopPCpBOV4yRl/+kBEk26xwR7useV6EOSiY+aMAnxxXdolT8\n/SYk5YAvB3akpasQPkqcR19lOClAOl9xe9FsWxc09ecH806TI95Jjdv1mnJawsVow0DWG3HV\nd1p+Nf9Lt1yMpz5w8GtEZkd4L5Q98FJdV/aC5H9oTrpanw2kgiSNwNDgKY+AFiAVYEEXyfWa\nstfIa+JhaEfSOSIhiiSy47e5t0ZGV4HrNPx74MW6quwEiT6sLZ11rT45SETIg3RvxOoXQCI9\nsPQWpF4DWzY5Bsg7aBdJUTLBps+6J8mTCw+8WheVfSCRPzY80iNtofW5j9yPvZYVZFBcXGYp\nYqdTBYk9k/wDHBG5sSR88liaLqszLSogpVAyQNoKEkVo91Bb4CaszZluPINkykyz3fJLixH/\nrXBE3i2NOY5Tn1SAZB5znuf4AUkGpESRPBXjwKt1UQmQzlcsP9+TZzCfeBDqII3CjFJU7ONJ\nImlaW2lxdNPHjkEeIlWwSgqcXOMy/+mrgfTWFokhzFH4KkB6oC00Q3udXbGAzgJykBDWDa3j\nCPTJVoujWx6NFX+Es845D24pneqRZFbUgVfrorISpOpRBC8CpEcac1EXgOTsv+gHWSdQ9pQq\nWWnrhBTVtj+axmMdSDcBKYeRxnGmfwOkLb+A9wwAACAASURBVCD5xMNL5ZOAVJT0yCdrQRq1\np9Teo8IRvjnDEWnyWx0Sh3aZ4TL9rUtHHHm1rikHgGSjvtfK5wDJux69nJtAGkqQGpkGLb6z\nb7OrqTiktzwaCw5JqoS4ws+2ZlYdP/ZyXVAOCO2K7RfKZwHJR3e6/rAHqdpFSgfp2/MO6e4q\n0kbxATgkghx4+qkEjmAdcGC2nLEh6+sdfLmuJ/tAKvMLAdKD7TmSqm+P1PY2dgrtbBdpirnS\nRvEJLh6kKbobVXpIun4DI2vrkAKkFSBJiRAV7bxUPg1IziXxKl1lv0N//stwsJJsqA0j8Shp\nlaSM0f1I6RMRaVGD7yIZYjH5oYuNB0gzIF1EPitIVAdJh15LL9MYl8VUt7XxadSqbIeBlHGj\nGxFDhR5LZsuif9QshuXosgZ0mARIF1Hsl7Ia82qPdY5KAMynpInp0Udc+KSKiksCMtgJUY7s\nTNhHxh9pXR9nvpGjAClAepliPxOPeNnUNRwR1J1yrbjumMJALvh2D2puggQuiftIxEmHiSpb\nfC6ZQAUJnsx0WQM6TAKkiyhGm4YBBeenqOwcgRvAT3XW6kjmEwCpJMlFa9otwhmyFZBAL3AE\nIF3Wgo6SAOkyiqGjnrINH9ZPpeqBaq8GSROnQEN9tOj93aDUckh3PDSgIxlKYpMgn2XX0M49\nR11+FT6zBEiXUay+REtXXcFqNZmNm9Yn1TJz3ll4zBogyVCSmMSdJdlR9GH0CKOxlzWgwyRA\nupBimMAnbxlnMQsSeibdKCeuZichJNmPS5DAJVmjcJNjmWGSiUhfJay7S4B0HcU1j6SQDAsg\n2U6WvFd0qrKNm+QdfupBktjOGQhB3g70sVJ2e18gqpskQLqKYrF49EgcnTXT3uB98D3jkjxJ\nyVkkayepF2LwyrQdFNyJL0r1rUNBUipWzY9Tpy/jjwKkiyjOFiy5gql7PtmrXYNrhiPTV0Iy\n/Idg49lzwEdF0g4K7NQcbuV4LOgzjX+BzPckAdIVFGO33YVJQ2mtBqQKR2MR4xmQTEINe0l+\nYEi5cZ0k+0xmk26o+bxDL9VVJUC6gGK71oJxHnYVhgpIGlNV3qzEfa54ZwYkz42Chc+SNSDx\nyDAroC+R+Z4kQLqA4sbMCDuxnOqTXYUZTxKCRx6k9ypIdYf0lkobRu+h7N6caOelGnIAef/v\nyEt1WQmQLqCYjD2qzQ8GpIXJru5N59pg21cdvEv8Z7wf3H0BKecAYWBJy/tGSYKPHNalx6hf\n1ngOlQDpfMWNNRblKRGah2i7JOeRHFgjmeGmukdCkNzt/4lNcnGwcKSu1zCdOLkUR3JHEdoF\nSK9T3HI6Zu5RI7ITkOx7xUCsDfRcH4mKxLcHKUVtZva5PTs3WYPjui/DUYB0AcVNkDDWaq0H\nxNVt/q1aptx5JXRJrf5Ruv2cRC85SqflK5E4Af5FVlm9S4B0uuLiwV/oRXTluFZoNxYgZQ9T\n3xNNvQGS7SEljyQnVHBUGRAeKTxSgPRqxf5RLGiOuoJ3m6MKSNUiO7sX2WyDn+zK952rGLDk\nCDlCLzfCPimdQVMt0mFX6tISIJ2suHyYmAGpStgSSFSP7UZ9dJ+P7WzGjgO6NFeCUhkDYIK5\nOtQ68gOZibicL0AKkJ6l2EQ7taeFbQNJ5wKhSftukwWJvYYkG8qMHRKTlm3gc6qEdQAZbqcM\n+OMXuwcJkF6teIrX5EXJkSKz3iOVuQabpzPMFdPAR9NF4qfyoZO8aa/LfFC2rrS+51zgZW3n\nWAmQXq04DQrl7RpH6JKaVutAco5BnEJh6eyJqAVSngprzofT36kVeD87IVNHIf4ua7us7Rwr\nAdKrFac8XNqscjQYo1wDUpGzA8YccSVHhUdyqYchPX5MCDW16CTkgEPKSrivdNQlv7YESK9W\nrCC1OeIF1TGSmgOpdEdVkLRj5Asb5kFKJULcespATCcEvtM+A1CGeaP6O0B6kmKS2K4e12Em\nAk28DZIZaHUf2VeQX7CjSC6UKx0SYYuAub7LAeU45sIG7iNd1ngOlQDp5YrzGGsj0WD2HMXG\n2+NIJlNW+RDS6SbhDSCt5wjZwQhUENLEII9lBUgB0nMUc7VCI7AzuzJJ7kFeDY4aO9RA0uCO\nbF1DcV4a2I1KSm5YoeJ/JdExcuftsrZzrARIL1fMPqbKkQVJSEow1Tmq1eiYXeog4aJztqjP\n+qM0i69CrnnPocWjxAFSgPQUxc5s17qk5hpCGLotg1SL7UgKv8kMtoI/sh7Jeh17Fu5vjvCO\nuN6XlwDppYqJ3ctKkrgKIXuHCiRLkZ0N/iCo4yQ1Pgi2ZJxn8bkWmVBNKTjEuHD2/t8hF/zq\nEiC9VLGd9LoCJPtJzduYDn+FohF2kQWEOKib/mRiKtkP4sjONU4ZFvkHy5KIdTJlh1zwq0uA\n9ErF+sDVlSRtAclk6Iw7ItgDBpNkqkNWWsZ1pLGdC+5Mxk5oNekGGaj9EigFSK9UvMYhGZJ8\nJroGUjZccSHKTvYWEmexs8CeUgbIx3Uc6MGTxUpA4SzYBRGc0siDTSBHXf/LSYD0SsUrQdJR\n2TKHhqIB1FihgHv8YtFs9oBR1kIusNO8A8GcWA+SG6Qd7akZzAX3Y67+BSVAeqHibJvLIIlT\nKrPRJVeyAgl+ULoLYECdkZzYh1tcTzZvbZIMJMYVjgVIctQhV/+CEiC9UDGHX+tBKvLlqw4u\nQCIPUiXIqivBBU8sSO+wIJ44Kd8fK5Lyl7WkRyVAeqFiKuy0TgE5b0HGW6wjyQwvWY7qXRWz\ngl7NI1mXxHWpyir3iNRJ4QhtgBQgHaVYJyAsUDBzaLu2qECpDlKzw2/W19JWbg2ScPn96ZAc\nLo5IE5XLgu24bl1IgPQ6xWqes65kwV+s7GQxSdD3nwMp0+Nzd+qRyBevvhuQJJUOGjHWE+07\nrlsXEiC9TvEKkNrNok9aQxFpCsC6hJoxS72Fy1lwySqlNB50k9gDMau5Zg/Q4rjO5Sh2XLcu\nJEB6meJ6J2QtSB86llspQGhwVM6sqE1sIB0ntknBPMscgz7D0bvgQ1p4pNFeJWP4eUkKkF6n\neEX2bR4kjb8KbNzLyvCRcGSNWTHxp1UL9SRetNWv3EeSaA8SD8Up7LhyHUiA9ELFyx2dOZAo\nr7g6oI8Y8lwlbGNkP1Bkn4WDyhn5aVL1MS/Oa7sycrO2FyYbKizvuXLXlwDplYrBTKvTY+f6\n4owPm6fd8k6DHYLroYwWJJdZgBOzfyokkSkiB49kB5NqXnHXpbu6BEivVTwH0nxKqxLAaUvQ\n/VJkyuIg7eJQuWoENtfM1OcE3Y8fP0gy3vDAWE4/WHIcSJe1pkckQHqtYhOUOQtddWAFPwOS\nGi8V0Z3m3KZdXZsK0tyM2ffsgJgknpnBWYcKOt4pPXLT6OOiHAZIL1bciJkW25tJmQ88iopp\nNe3pN0EqchYlRz6PR9wduvukH+CMMlkc041OXI3SrkupQSV9XA+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4V1dw9XllhYjS0InsR/71XtnRxn6QclQXIF1BMS7tC6Wp7HXq/gin\nWCBIpLkz8SKpeTPu62I8seUbosQdIjfsKzsjSEU1kljajKUb53gIRftSFQ+A5A0iQDpPsS3s\nRpJqeXBfKO7sfBzRHYyAUJHJaHA0tZ0DPJYR2tYUB8A66jyoHN+J/jlLl9D0IG807Mud7wXJ\nzSyatgKk0xRbOxdrr3kjLFzVd9Te0z94p7Ec6OYCRTeGNEjNHVkZKiCNUjUxZqDwUUyaea+5\nPfvqOIZSg3uO2QtSaRAB0omKcc4ERFXVYVnlR3aU3gXTVAcJqicqICVgkE3ZGiSokw6SprhJ\nQGJ/RDoYDIVFqMe/sQuYFhMB0nb5LCDholxzN8IWrgpH+kC76cWIgR0e6xMXOCaL/ijtKkBx\n/QJ4KKiwI05lwCTC98wztKw2C/8+QfZRuROk6nJcAdKJiteDZJkintyt/Rat2hmtN8pephYR\nikMzzOWRp4HTDTktZ5+JJCEer+7KH4rfkTHYbLMP2PsKIva1uxekikEESGcqRpBmSbIOBcOv\nXN4jOTbASGnJm+lN6Z5oHk53zzV/Cc3keQSkNCl3ZISUJuUrfZgatRau5ULPkJ2tBkifQzHk\npudIEnIcVERvlP2JgoQYYQPcE6KacUu7pPV1CqY86TKNFI1c6gCJdhiI5WVdlSdxfUnZE1Da\n2+Y+kChAuphi++zveq4uORTznryfQOL9OBNQtiemLT7MmJ/4qht3qNDHZJBkmEggcv+DnQ1a\nqKqJbjX6w1na3d5OkGoGESCdp9gOsN74gcYcjt3UF1neJoTephUJcQeI5G7ikGRyHhbomOBr\nsC4pgSepbwndch0fR3QjAoUTkjJJJFornaODUdrfWID0GRTbMVi2fR0+RV8Eo6uEa3qSMACI\nkLyDQRUgxO+IId5YMzFIWsBgV0IG/wMld5oiTx4LosdqSHcoSA+0FSD1rnha7ZMREY7w2rNH\nwbembtGboiT+J+UbxLDAvUD4pjYHLip/xnk9bo7jMSIESWixIiSlgzSss1bundJu8/c0PHBo\ngNSxYgzArEMqOfJ1rBkgBgmIQZBwnBdBInRF0Gu5QZ5cUJLjOLWtI646tWKESRYk7s50iMBs\ni4qKA+QhIgOknhWTIcZ6nOJt66UkossOiQ+TyC7bVgWkbHMJqGEYTAnpTdIaOaDkp1PwOBL0\nlHSqBAZztiaojOxqr/YDUORL9rayC6RIf19CsQ3ZLDwlPwa0NwvSG/SPTH9IB2DlDVKTL0Aa\neIxX+2GS5BtzPR0syk+u4Nz4ObVz4wPB/NHXPZAmoMeOz614QgKkfhTzz37ZBwKnpE5CP8Yc\ng4Ik5kSQLCtBguIecU8EjoMTDgoSSRXQCBBxWMcOCeq8XRhHN8axYv9Z4V4WjupgWXCIzISq\nGZCiROgCiifjSRgYUOxDLzmFUOUo5xsykmKe2TgGBgnfNDNXNb8HJMEYUh5ONVOcxrSyOLq2\n0S6+5bMJuYiiKJFljvibbqXicUekTRmK1AkHSNdXLBy9FSZ1cyt0GcreHEgY2xD+8BPbr0NF\njACfW1kFSbzbqCND49RXSm5o5LfS22jhsH3jlEnJlzOzbZ7p0Mw5YKTdrkWWIrS7gGJyJHFP\n5yajsQLSrQmSDep8BGUcgQMpd5HM4NLAngKqVbVfpSBJYJdf5NEkMHE/xJu/l+WAPEkC0yrb\nX03JmsbyRRndKQZIPSi+Lz5DGqC9wWCr+iQHkh+FZcel1pV/UyFPYAxPMm1579Is0RkNGuiQ\nzLxlz8EbuZdkjpd2jR05662zJD8M1tXiYce6o2GAtQGLM1zFUYB0smIqwNBRJYIktIrzRtZE\nOdlgZIDPDEikA6ZkObhBQ4Mm7rRAlT0R5xpMB0n9iuPEGz+1UDJZmOIL6eDWYcIVTY0zrPJk\nlukOkM5VbMnAMSHN1rFBvVX3tCBxaiDfafQUHiRN7Q0DeC4Yhs3wDDyYRFr3A/bFA7Nie0LC\n4J936X/xKynJKlKaRIS2D+XI/OSYT7jjOc9RgHSy4oIjrT5lm1GDqvouBSnZ+FCpwGZjAZCk\n8yMWLb/3U3NTg/IzTeqh1Ow41COnBrEpoSD8mTewrJSGxT+G0VwGsOqTiqfO6laAdIbi2sPk\nwQ1hjRCJ+7Ks3QQCWNOn4GjgUSN2VpjiS65BB7Aw/0sYALLZJWCl4JsMr3N0KKvk318LUhEh\nHgHSin0IkivF01cCpJMVly4pZx3egKQbYyO/5MCWDJ3iNNUqSfgB5xkYEeFSIjvtTCVPROJO\nBtTCm2hv9tw8SS0jW0nSwQ5prYOTn6ppa8YgAqRTFFdBmtJ4OgBDauYVo8suZMCBHvAmBGGZ\ngkQWJNaVmhY3ZWLANBDEzlLJNbzS7YbnW81vzxCyiqXjRmHNb8DKI6Z/5wwiQDpFcQ2kFMNh\n+q4IhPy2BWnkZeUQBSAre5gSpJwhZLMRQkyYycC58E+agROuDxS1CVnD0mGh3UaI0iH3f+YN\nIkA6RXEFJBKQSP5nS2aLK0FKRq2T8EjXT8A0G/srtiF2V9JFAuMiRoTEY/H/YvZCJ4MEzu1m\niRdc5jlZ/PxAkHYeN2sQAdI5iitpmLmhJwAAIABJREFUO8Up5wDAIeAy4fCuIWWUteXEC3FS\ngHtIIw/8mLjv3hrWnmpwKCloM7A1Svu5BEK4nkk4LIOkZ1Pl6DiQdh84ZxAB0jmKy3Gk/Jdp\nkkgJOzLWtgYJ7N4lsoMnX7K9YzIvg6Tp7Fuup8j+C4ZgpZfElpwcUM7bsTuCBzwX+cRNJNlE\nfIWjQ0DaHtbhwXMGESCdo5iQIV7KBIGCn2gzRANZ8enmMkHePUG5KTgRA1L+oZ8ak/zuu7Rl\nShCI2U2gcf8oey0bifLZFpxU+XDQ1Vji5PwhID1w8JxBBEjnKLYgEXmQ3sCIy9W4bjdZ7Sr3\njfip5hDc6T9QsM1phOyNsuFCHi63lDPhUqfAmYaUwODoT1yWxoDe/Nuvpfs1+FoIG+bJ390A\n5P/Yi+5tJkC6nmKc7Qr+6E0qWQEl9xMtIEl4lhwJChSZIkI8lJTtglNtNxi+l5YgetOsHMOL\nyQboQ3n7WQESKdil/QmjD4FknPV+jqppuwDpbMVUgiPvKEe33IWx5imBlwy2ZiuBZRwRJDYi\nM/TDZarcBdMMgu6OVq4OCJpKqYbs3RoR2QxJrgKvQVp17y0AyD+PSYB0RcUAUpG/g9xdzbDM\nz3imCJ2IdHLe3zkvgMk8NFxVAyC9539kX3iYsxm14tw3T5hdckm3Yk6wJ6n8vi3stgCw87ii\nnVmDCJDOUazZ7ipHb7X7QtrpTjEXGzCR7SWRDdDkqXojP3Jlsoub/OKDk8kLQfKTlYchJxS4\nEBw54lFdya7PYZC/AGQC+fcATHW2gQDpcPk0IFENpLQIpLdA/b02naNxVJDeKxwlr/SujwHT\n4SIYax00kf5OfFh2STA1iXfitDrWFK0BSauQqKRoKEDyF2EvADuPK9qJZMMFFbND8hhN3sg5\nJB/zcFwHfSODkvFH8hyJ5LQg18BwYqCoj3ARWwevJM8z55gyN9UAqeaSZg3cB37u4L0A7Dyu\nPLs5gwiQzlEM469ufaD7G66Gwd+X7CHGvIwCx3Ua4pk+E/eR8ltiFjftc+V3RrN4HefA02ek\nYSROWx/QI1VCucLJLFnr4SAdVzUeIF1QMfeRfHinjqJhT2LneWaQgGLWcGS0HE2JjWxfuTUu\n45zA1NiQMnUcR+rQrpBEcnRJkvSC/Lkvmr1K+fuxx/oP4yhAuqJiUmowwqOKOFPMwzhar+pQ\nyR4JXBFB3GfbJWBlzIUN/PwW3nsYBpJ5fL78SM9B3uDzvGX88OTXGz5RmXrYY/tHzmIKkC6o\n2OQacFFvglI7wEcw4vFQ4uhOH+9a0JJevJPtQLGJ5QK6QXs/+mBlaYDn+mViXKVEgRazpJ/I\nL8Fmm94MUunwjuUoQLqg4rxUquOJOOKz90FGQtMNBS80wrLcNttAhNukL7HTc8sdLsjZMT+8\nAh9zxP9BoUQm2oKUIzwiQrBWuiNjt4U5Ltk5XCOp+9iodFbBrEEESOcotjVC2QZkWMkGRP5n\nFvoqxJwYlMAJSaJBn0oO+Wy65WfK6sTA9/SPnCa7oiKQw/+l/ghI8lxtM9paxdDs7hl6feDm\no6V1pZJZgwiQTlJclNcRZvAwrCtsRn3CCAk58DuknSQCmNj+ib0QEam7m+rs1L/JecLMJtI9\nZUQJK8stOvBy+5TUWllHfU/GiN/Ixx9M0RAgXVRxZQRJQcL+UWE5o3qkkX0RjsBCouEd/mUE\nFAkpohsIMOGHl6fvClkE1awFsACQJYlu7vUWs65yVAWpJIbKtw6SAOmSiutTZMs+UgsksXn1\nPCmq03/BWeVXLk2gODGbeeek40M4wnlNDidwPKMjx5G03mJ9ne4MSPXjn8FRgHRNxY3SoDdc\n2vvmawG0/hrrggjQwVhPEJKIDxPVXPYzNcyQMJlplw/vbmQqkqBjIjiLkek2aW3essFW5jY1\nQHoKMM3zmjWIAOkkxY3qIJdpKGcacOZMRk9l/FTIMYlw04cagZJcPacNQ8FRwYMCZcpWrR8i\nOWisyTqSqJxUn8rGS5Ce43jaZzZrEAHSWYprJL2RKw+C1b2z+wCTxnw3UlUkwjNO7EQw1sqd\n9VEhI/Qs6RBCtBxOpUuqYzR9smSq4oy1QJdIHhNd7Hw8LLNnN2sQAdJJiqlW910u7p0lbQ9a\n2JZdh+sLQe6O/7VPfiUHEqGMMmIktFofo60AMltAmjf9WoVRen8ouXk5RwHSNRWTJ4mIilI7\nW92Q7Udt2sdwmRxlBx1UDsQAKB5uyY5KP8FOUDpmBKXgq6ouqRHZLaBkf0HoViT+2SXz5qs5\nCpCuqpioGEty1UFloRrJEvaVkI5DOYz4ECTjfTB1h0k5V7ugTswdpo4JHNiSS2qTVC3NrexF\nPFP35RxFrd1VFdvnteQuEvEjliXAkbsx3cuRx48qcIgPMgTh5CT1MSMHcZTpJIuBZvikiELU\nu64SpgLxz3qSrDfSn42qNZ/hjLLqOYMIkE5TbEB60zWLiZ1RpfBbOjHW5ViO7vKDfvzQ4m/g\nTnxHdiwkER6Gc6ZnpNavn+kfbGo0TTRIKuxzWznQaRJFqxdVbCZPvPnyIHI/0zyGJGGWzW7f\n2bmjkzFKkpGScA9AAq+SAyUHkjoadQEmeSfw2r2XQSoy+jXTO4OUBYk+0kUV+4WEZJFVJqc0\nLTZcM3hq6ZnIEZR+MEiZJM4qyHwIIg7vYMyV+cmJCDtLln3U1EZ1PHYeJENSlaJrghSL6F9V\nMWFc1xqQRY+U7qfYrLqlqQZB0JF/hCWN8EaIBUdhChyLeStPoWBDyn9MEOgHnVaABCS1OLok\nSPMGESCdprhYpRjmx1Y4AstW75FB+tmYBnPZERmOIDHBKT5N1BF2kUYN9Sbz0f4R2FRtGKnu\nnBokNb/nhUEKj3RRxSVI+rpcRuQjg4S9EcrO5oMdEvSOFKSixgHmWeQ892Qo4C/SjKX0rsR1\naFQVkIpioUWS5kzv5ZwsSoB0UcV3U6qCVJQ3JJCmI3KBnB2VlS4SuTRD+otFd7Di0DuvEM6o\niI1nu8l/iBQrNSoFqZZiWHBJrbLUS4MUyYaLKk4L2EEXybJUvROk8RZBZEdTSuHeM7J9owkm\nuxiXVA8R1jrIqg2cWsBuDEeVxqiUHD8cu+ySZn3RZUEKj3RRxbx8kF1GqE6SBUnzBBMVOZBL\nIBEhSxzaSd0dT4HlGbOQc2As8jR0th7iUM9aFdBS5M2XXNIyR5cEad4gAqSzFOv0cgnyDEnm\nTkxdpA/2DmKReQCJTJ6hBpLMmiVeGcWEhZMjSgAQTozLiYeilIBM96gC0gxJK/zRNUGKAdlr\nKoaFuCoglTXgkktTkoQPTDB4l0Q5R5fKhQiT4eKqFCTM2HHSocERlAhV2EHgt/qjS4JUDe4C\npPMV48OXa6k7w1G6jxBvsa2KQ6pm7LKnejdOyUy2eEeQOHJ0CkuQBJUqUXNeiVY5pGuCVInu\nAqTzFePDYs0Ykl+2wSzVNuShpOyQsHBV0SlCOwDJTLsgBUknVrBHwvmzxpxgGInseG5Bkgkf\n18Z11wWpuP8B0umKWw+iUBeVIFIrxglJVMiPJkhkQYKYrkg6SCAnIV6iyS4cwZjkfzELXgMJ\npkOti+sCpFfJJwCpwpHnCZwR30fC/glJzsAMIZWdJAaHuI9ExE/J9PNn2QsRE5w9FJ7EaEEi\nzDz4YK6YDNU3SDMGESCdoniGI+wllb18GLVhELjmW7D54YWdkKzACp6MKxxyozj9lOd500Cl\nfkjagUtyIGkkeVsagu0BpJKkAOlkxcscpV5TeSsNSJkJ9UPywvCkHSJxQbzTOxc4ZO8ifg9y\nDJK3c3k5s0FSls6jxTpt6n21J7o2SBHaXUwxrXBIiaXyVuJv/eROMGnHGfDJeLW0AactvROk\nydOODqRJz1ABSUJAcETG/XDXzc5xzw6pf5Ai/X0txbSaozeod0v9F+6QSO5aQzqFCcK3MUV+\nkG2YEPsh85YQJFEziGdK6iWXB32isgoIokUvAdI1pWuQaANIb7m/n24jh0yS+DaRnZQIqfmy\n4UO3SDkSs5cuDllmsczOOKN8IgVG7xWE9kV21wQpkg2XUrwBo3s/CYdDTZFdBuk9g/Qurgfs\nN6OnlaoQ7SmB2SXBAJLgkxDRsSMXxhln1KDo84AUHulSis0aXCtIchyNI0FoB/2RwpTZ2PlT\nBukHuy6FSj0Sa0p/RgapSDR4F9WmaE9kFyC9RnoGibaBpAkHMrYsFXPvGOVZ85UwLFP0rsny\nNOWCSeI+UllkJykIT9IoYeYIWe7PDdKBoR0hlFSdofEK6RkkKA2yzxabB0k6L+ASZE0TWdsE\nDVoiNjtxKU9dmjCCnpKMx2ZlpluEgZ74Iy22W+Lo84B0VPqb8FDz4rXyCUDC7vxicEck/RQo\n0JGiUwUInRLnEMDUZQLTD54HaLpJozo/7hQht1iramUBo+0cXRSkowZkCY8l19ArpXeQ5Kc/\n55lXgJR2VpKmgO0dsg6KEkOjHGl96/u7mQqoFeMWJPF8UMegMiA+ixx9KpDqSExbAdKLFU8g\nudsDjzhv95FMoXUK2LCG2zolgY65A5Cg/kGcE6QVBnx0jOsXrfNBFZI20nRRkAqSAKSxLXLZ\nzFEB0oNtkK9YgB/6NkjQX5EuCue0jdHqdD92SnAYOy0sJFI4pWsk4WPbMLaRRJ8EJB/crQSp\nakYB0qONUFn6w7epNreCPwIr5i0I5dhm72EcT8EYFSQ1f1jpIacbZIekp7ouUGEbm3zS5nxD\ngPQS6RskXguoep+qXSRX9g2e4b0M7OTXv5hrxys28I655A6aM/5oDqTNwd2nAcmaQIB0quK2\nmZAP76bcKA1SD1r6BQvSqJO5fUmcHbSVNDjsMGAwOGp+u2YbAZI1iADpDMVtj0Qf+sQx7p5S\nyjOMxsjZnA0b035ijGQfO267UxLYmT6UY899gO8+laMLgWRXmKUhQLqS4mZslxwQJHjS3lhE\nYEGaHlXM1pp2UGskWY0hl/HAig06EZDbhb5XBRwX9G0EaSNH54IERcIGpPS6YRBbQIoB2eMU\nNyylrsBauXUM6fl+72LnxdPJBCfbQbIgeXaqIZ22tTG06w6kDJHWk+j8LBvb7QRJqoIIX7xe\nPgNIdVOpX1IeQaqRxLy4d9QkR5kPYSuJhKTRFs61URLkUmMbOOoKJK6TgoXJcAGYQ0C6iHwC\nkJouqfrrpalucB3phXgeftebpP62su3zHwtSzSs15pYvzpt41CGdB5KbtyLl8DBfuG4QAdJJ\niud6SW7PEfokBGJ+6yX08xwl8yBAiQxHpoDP+yHz2qcMV5EEtHcBUrkc5vQ2PlWgahAB0kmK\nqRncEezyU2OtvlpiNLS9EiSOTAi6z5LmU4dUnWqk0/0Yp1rufR1Imzk6DSRq3Rbcp2oQAdJ5\nipvRHX1o9m5uooJfIDxjoO9kj6S/pgNkHBSk+t3Pn8hZOHdVnlM13NvF0Vkg0SqQwAoCpCso\nbt625JUgtwDT+Gwdg7W+RJG8a9dqVa+Ujy0ju4IkNzZr4rwqR0XfaRdGJ4GUT395v5pBBEhn\nKm7ctwySkgRrBoGh1ow0rc9KClJySgQ8mcpVmY5eJamI5mY5AnvsCyTOxqzdvWYQAdLJiquL\nQKYP2JpH4UdW4UpG2jTDvN6xWCWsPjzI8C3/AovDWYGSZiWaHA0Cay8gkfzUrNy/ZhAB0tmK\n50HK/gK7IDqs2p7lQ+KWbvJYGBlOyhwhSLXQbapiHdy7/HeOowFQujxIhLL2kIpBBEgnK648\nxYs/II667HwJxiF3ieqGaD6y3iI3wUbEHKUPlV0TDQpA6pFsjV9hg9M5zpzhFUCSQSMtXFh5\nHBtCgHQZxaUF8gdsuOBI3iWyywy0o7uGXQqC+YVCkz8e8zqR1rLYX5nkA3DUsrdhH0dPAMmd\n5MZormzNG0SAdLLiBZCIiHN2EN5B32YNSHasHu1H/A6+McDvtLFElTR3qRbUFRZ3Lkj6C+FG\n7ugRjgKk6ymuxETpfahskx9+WecxLxIEs4+qHE2hFZoL/xDrK7dlU+UNIxo0MnwOR4eAlM7z\ng+AnYNC0y/TRbjWERadpK0A6V3El15Dex9BOgyjONmBVT2mGUsuaUVJ/MhTQ2E1lrFEs48zp\nfX6fc0HKtMi/kqnka79fTYB0NcXkb5AmG2Q01i+hChyNtpgBOaKbzdt5N2QCHSh+0LeWY5+l\nHU4FabqIfJmxVkQv/n6X5A0iQDpZcdER4fd5mJR4GqxGKBakwi3RkAoczBw/1eA37AvbJV/y\nSsugnQdSZTnHYqrq3l5S9JGuprgYf4GPeAKQyTJwkOfvFRghR3I3+x5ryH+d3nIr7fSQSe/l\n6DGQJCW54was1RAgXUhx7ffQxB0JJD/2mUjy90rrGKg0YZO2s3/R9optWvY5C/Z2Dkguglu4\nCzvaLw0iQDpNcTWssE8pEIdUFhKUd8tyVPdH2lPypoHJPLPzIz3ynRg9ANI2iPJlzkeu16HH\n6laAdJbiVnJZPp8DqXK3LEh1s6S61ZDJ0inhZF5tlleDxAxtXAMB0+OrlMChuhUgnaS4dds0\n3ZAKVlc6JAGJW0GzFCDqgZ13U27gZf/w/26SHgPp6FtidZgjdCtAOkdx+6aZ9Lddie4917hV\nJz3cdErsYLyBjqEs9JB4sBJ3MC9WJMRNs0u4EI4c28TjLnmMoxUg+fYDpNMVt+8ZD3nkm0CQ\n9xbxd4or5nSwyKbxGI4GSGCHNLgwb1DQtKhhnVkvYZTFbe0EiZOVD9ySzRwFSOcrnrlpBhXZ\nNq7Jc1TWZBqOwNLytlVozREdj+1SEb5YtroFjHRxc13n/C2RtAukR93RikqO4iDdCpBOUbxU\nogZ3YXQcVXLfxfED8VxZMA8M/NzuqBp3tlV5m/JbsxwBPZXHBmwHaTrVrUkGez+WVc4/jDlA\nOkXxUkWAB6nyeFh0SO6GmzQU9nDA8KwZVl6YNAPGjetIqqca+MRaFO0A6Q7RBw2NVWrX3Y2d\nHAVIZyteDiIqIMn8Pg+SN2C2exeJVYZlK6/KXHn5clV2661EKSO04mGfywrgtHJR6u6bsUpf\ntf0A6VTFK26cBwnmgpddJGtVeUsCMf1wqG36V9TmrRYkNk6fn+ZJRFBzsfQEd2VptnWr6dFb\nsZLaqp4A6UzFq37/Co+EN8beJrTvMpCrOaQ5kIzjalS2todpa6EbbcVoE0iP3Yn1zi9Aupri\ntT0M8T/k2bHrNEIKoZo1qH5olTnV9jz0fPwZtk7bEUNbMdoC0iMuaWMIWWtBtwKkFyteG0mQ\nTKXQqK4ESWCDFNuAOQabeMAdUNcw96Fvvn5Y3rVGxX18aAtGLwNpvZYA6XKKV+aO077ijwxM\nZdxHIyE60x80bxpmOGpmG0x7tZNkVSINLDZitC2023lTtqU0hnpsFyCdpphLB+bvmZtM4QiC\n0Vq9WTK0X4CUm4TWC33wWWWsqXm2BPK2mZZjQNp3T7ZSNARIF1M8WXs5hNq8ZVQTxshNOKe8\nwmoyMLE0+7fGBTJT1oAvgb/d4RwH0v0777wNmyUGZC+kOHOwYJl2/5HTDRrIOXc04soN4IHK\nnpPZGIp3pCxPBqPk/TkDOwuk9OOx9frvnmEeJULXUSyL/0CXxpupfZRVfjS5jiA5v6QgmdI6\nQaFIf9eSbXYDASL/Vs3ATgJpD0crAusZhW2DCJBeq9gUo+bffhqdweP+km0QL1RCZEECG3Mj\nrfhnqHxUOYzknRnzOxOkbVd/N0JZYdFgeWMDpFcoxqruacFtfKNiHgwN9ovqjzOCZ0/kRuwU\n8zmHNBv2DVUq7cfngLS5d/QIR3XvFyCdo7ic/2DSBeaW2dyCmVcxD1J2dDcLUplzMGZSbJSG\n1LaxwzlaBdJWjh4I6YZUWx7JhosobjwaDxCZblneZt+D/44+x1DhKK/HpbXWyRLyP3Vjasyv\nMLs0jjjLIz0/rJOr1w4hA6QzFC9wJPhAoWr6A581QjvzMKQ0JdasWpy62DMB2kaQTEPneKQd\naYaNkqcqz7m+AOkExcsccc+Jwzeb5pbgomyIeJHiG+Bmpi8MS4k3Ns4Zs0IbxsDxpNBuS2y3\nL6zLV3tGTYB0guIVHE1XPa9yog5JQMrtlD2tm86HzU7pVpI0P3TlYanZrWyYRPuwYorRM0Da\n4JK2zm/izeWGV93dAOlIxWsc0ihTYbHimztN3NCbnVOh3SHF5gZcZZtfKKawZUX1ffKeiGeK\nGM8Baf2V3+KOdMB2BagB0usVrwXp3YOUs3bazjRjTjmCzpFYuPaPOCe+WN63PFKZS4/sjNc8\n6zX9+1qQ1t6Nje4oX+VVDi9Aer3idSDd7x8vywAgYRXr3c5umsOzZo0uSZG6zY4DMSUrWLMc\nsY68zsKBJK3rI6298msx2pwJDJDOULyKJMo+ydR8m/ubQeLPjEV7fjT5MLhMW8WGFrtILoNh\nQEq6XgrS8R5pe/1rgPR6xSRT9GYd0qgeaayBlBdDuMkgE/aEXGynNAEtTSt6CKQjSILDF21/\nXeSVL9lqjgKkDhRnQ5VHmjYdEj8ytuqRsrHlvNzdI0mq7lZ0kgqQZusThtle0swydQpYe626\nGWjyx/SmC6asAGnLlV+ZTN9xdwOk1yv2Fd61Ky8OSZYpztNfs2Lt0XOe24wfoW1XOZojCQog\nqmbW5AgjyipJ5DIRRJyg0IWGVNyqls3zWX/hn8dRgPR6xf521kgi55BIlj7hVBJaI1iyI8dx\nVSBctyT4t2ZnbZCWSOLMniEpvZdOM3lTGQLjL1CfBbKVo5XPmNg50Va3AqSXKK7cTQjeAKT0\nEHMtVCW4EzYcwvEc/jVHH+THTUFxw5aany2ShKdSxHDcfzJeR94pmmA2eWc9P36x0exXuLe9\nNzZAerHiqhuQUiCYspdfkL45ikdyNmpIupkhWR06pZKjoVm5Ouwlicy5lHEd72OxoZkm5Rsp\nSiRVhNuv/gJHe29rgPRqxXWQsk8isoGcXH9Y5SQPERZGagy5qN1p9TZaPmmBpDmUDFUGouyS\ndkhySzfib5Lg2nP15yh66EkwuhUgPV9x256t2+FITkHC6XwlSIgS2zDxjCRRUjWfhlG1P1sg\nydU7EMko7azjWQHTTSHah9HHXAL80RWPdStAer7i5tyFWh23rNCQ/mJ4U199UZ1RpggfrLw0\nka9C0lwKvMWE7+GkHtAD/BQK9nqjdAPaX2hni9ywbgVIz1fcBGn0l5+s8Fw/3r8C0puCxP0K\nthH4swGkxhG8xyqQbA3tUSg9EoI9h6MA6YWKbe7J30g/I8K4ptGu29UYpWFT9g+NTK5pS2i3\nonS1Tkcl5XYhij7qID2OUYD0VMWm+wrupX4vy8jObJrejmaUy16ShHjYOLqzVSTNHLEaJOjS\nHIbR43fuGRgFSM9RzMT8vPP6Eu/cMkiWJHIeSQsBACROgzNPForZBF3tgyWQaoTMDAYfQdEh\nFl98jwMaDZCeopg9gEz4ZsOcsd5KtsFQ5Ybzif7vLkASwJutzmnbxlEdJGy1ausOowMDu4MM\n3pJEx8D5ESA9QXFOlmVb0+flzda5NcqEWigpSGa88zZZbu4h6RCSQ2AVR6ljZU3OTFNvgEQ4\ngHoYRcdhpCBNd+i4VnUrQDpCcTa3/G9r4kJRb1e96sXS+Lr//2UBh5TLVx1IFYxRb5Mj6VmZ\nLWioHtmR/e/CICXUj2tVtwKkIxTzbbqpS6rZrDXs1swkzn2nrZuYAIAksyk4+33LNZ+cwp7l\nqOwwSEKEcBX9yjG1CbLijora2QNIOji2OxCiqVHdCpAOUAw/4rMgOQfVuu62CpUtXLpIGt3J\nqGfambtl6zkiEPuiOIoPqBj7sfDYxm/TYiRH3Lt0/ge0hI3qVoD0uGLoQUz+4AblbnNW3ARJ\nf48Bk/vf//s/G91pzo78UOx8ipspqn5eeRfjvRdyJN/+iJsXIHm5Fkg2p+U6SbVEHVhs/bq7\nsm599X/OIylI2eCszddBGhY+n5tlwT7slSDdDuorpYj1gIawTd0KkB5VjCM3AFIz+wyGvo0k\nE9kRT8zWj5NWG2bOgdT4uCVmSAxO6gUgHULSsfm63KZuBUgPKsas2g2GI9vDOOA4miQZP8N/\nrT8iXgQld/PtQFKrOOgBkEzfyp1eJyQFSEauDNI09wxAapKUcWpde+eVsHuUO0jac4LMxI1U\nbxOkVkJhPUiD0PsSjI4haXY5/J1N6laA9JhispHdRBBkG9rF19rlqHKkc4vIJew006A9JLVo\nULvgkbZKkYCsYITnU93hTJDm18Pf16JuBUgPKSbvkNgjzfboSQOvoqNEzFGenE262I50j3zp\n903LdCCibGYTtiMEg1ODurTC1jPaZObIXoekwzkKkA5TDGsiqEsw+e/WYA6YpYvpiLhAgG5g\nklxXV5tDQdBpUaWNCG4jSNyKwNRyNiTs2PlSx6B0iEt6vIlWgwHSQ4pzns6AZPpIi0noIuGA\nxdx0K1cSKd5wRk227QpJO0CCWK3lZPyp+ZNcSctTSQqQrFwIpFsTJOklLYNEZqoscfVczTjL\nlRZvrs9PLvN9AEg3HvCdIWJ2ueKVJM3vdXxg9rgESMcoJpNWcCBJxc4KkMjOQ9Kkd2GSLmaC\n2gewxuFQkJYJWOBIT1f2bnA0u87DBUkKkA5RjBln7KSYjv8SSMwhgiQckfdDxVqrmuEQ56TV\ndnVutiW+VwVlK5bPV69Un7BEvPDQnBYqYaJnJBHWSoB0iGLs4OsIqt1ezJ1xX0qmxOZIajoe\nbFBS3lWQkslnZ+RcUlP1OpDWYLTyMRRQhVuk/N4WQCITX6brTzIt+QRjmM5AtwKk/YrtoIlS\nZUBqVQlZkGR5SGnTgfRGXPBtRmN5Sp2lBDtJtbTdFpIWQdryMBcS7OALIIoYrVpUQeza+9Nh\n59hDgHSIYrB5/IkFkGwKTe04CYKUbF+tg5MN7IaIn3iMb0kvqYjkgKRqLfeBIG16KFLu/wFW\nN8BBPofSp1ueaPVGVhHJFclVOCYMAAAZyElEQVQvTzaIAGm/YnOj0SVhr7q0WshKGJAGHoC6\nCZikNiM/4xzoobUVWTqdKVuvUToOpNLEF1HCBSd8PkW/nP2OlYbccScbRIC0XzEpPxqRuBwu\nJqN56pxCRuRT1fDQVwZJ/RG9YYSn3FILpCNqG+ZBmk5u2ISSMf+bI4k08BOsVrR9VicpQDpA\nMSE9NynSIWN6as8GIkTA9Gc0tAOQSOI5jYFsM8hRBumoarv5hDQrF763+6f6Qsx+45IcBUiH\nKHbjIjqkY2yNARmKTLJLgA8wz4e0rjt5IubnjQ1WGrkhinRPWiRS1mQMHwTJNCMeZNhA0jp/\ns9zISfbwGEjmvOmMb3GB6za9UmuGTHQZ26mtOYek7/Mf4CN347m3YAyPMI7EJbhoyCARZ+/q\nRbNbQKp4UZxxpfvxM5S3sfEwSGeO0z4CEuHx5sXL5CIgfRizN1UGEMcZkuogSYeGSpCysbkl\nIRld7AlJjJVbnCFpE0i4TgP6Tt/y1FvaQdIXBYmwAfPidXIxkCAgu2lUlscP7RiP6drA2/KP\nQRPrGbSjhOgOAAvm0fOwLH9cJA43gcSNVPpiZkd0sS/E6MweyFEg+dZeJJcBiTxIZPpIkjdQ\nkzZBUsHR4A4VY4E/ZhR4YGbUyqWTZBdZcDa/EaR1exqFr+LoHEtgC9CtAGm/4mIMHiI6CNAS\nNBqQ6b5g5NxPNzuIucivL8+VI5IjyFrxGpC2uKRdIA31ZzkdixCd0kG3FqBb722RWL88lKqN\nvUquAtIHGzUY/60QqORxH5Akv0nrGmogadZOWJJDvMFr2s7Y9X48doJU+qSj3FTNLE+SlSC1\nDw2Qppf3VUBdJUMDJPxUfqDAHYn1aaFEpSKGB17g8DKHPlJOXMzh8AyQ/IGF9a+vbl3B0TkW\n4ORIkE74RlcBaXrrdsNZAXMgETswV4cANGTcSpA4vCP5dEgH+sgueyoPwP7Y7hiQSAu/H6bo\n9fe9KbtASt/Bg3TG17ouSNUATrYGHutBA8fozAzp+lnlYkraVm7B9/MrIO0n6QCQrM/eCM4b\ndhEvxtGBHumUr3U9kKigBknSUI5dEBo95AusD/OxkWJ0s63YeXxjFaQCrNU8bGDHHAcF3fZy\nbOLIyOvv+II8AFI5IPt6uRRIKVybAQl4whoE9SkwBGQayXkF++PMH/lgTg04gVTY/4tB8rW1\nu0gCdq6I0WMgyVc6b3rilUBKw7KuxqECkXR9wEDBp+S/po20u69pyJ8158Dm0G4RpLWA7AWp\nvRBXpQPY4ujVN3irPATS+XIxkJY8EgcmeVIFFAt4Y5Uds9dJw0FgWK7NiqnTSo+0GpDdHml2\n/sUalq7phIwESI8pNo4YC1YrHklSB1rRU0wWAihu4pWErgpItijIGPA42vGlFhBrXdL+PtL8\nTKaFEO+SXaJCAqRHFFNOMKTthdBOZyyBC/IgDWS7SehvECRhDHLejpkJJFrhkapFeDUglndp\nHTlPEs2EeDM9hksRFiA9othUu6Uf3gZIZH6WC5Jq9uoHmgAkGaq9QVBXpLUnkCoOp14GTs2p\nFv7EtsuSS+KLsiWmu1j6LkDar7gaskDHxpBjd1KUCpDIgOTsUUHi+nL5qPQzGaSVA0ckfbYm\nDnMfzh64hiP+gaFVGPGP2GVoCpD2K26YhyMJswYKklaqWhMlnvow1ByEDMhYjooyoPzuaPtg\nPjNYNfk5HmY+m5W1HMl14685dwPMEa+67TPno1sB0jbFTfOwAd/N+iPgShgyfkV7RZWYTB3S\nDfyV6Ujp3uyRJAdYS0lUYGlFeK8C6YPy5N+56+/ZK4pvnnr7KyekWwHSJsXteEVAqn0GHSnx\nO5ajtFWzZ4zsMO6bZsAUAZx6JEfCIkgNx/T80C6DdOdgYeioksox5dMB0jY5D6S5yoUmSNCB\nuklkRk6y0daCNQHJzO/OM8kKkFLWDjOCNsSrw2Ly6e6znbLVI30sktD4jZo+ynmIV1gBnpFu\nBUhbFM9Zgvljb7UZZVKbvX+SFs0f5J8mSNMMC6wLqoOUnZQZpFoEQrtdJhpcPG5WdoC06+rL\nSa9r5VAJkHYqXgSpdEhF4QMY+n3B7yk+8/NdnZlnh6SDtMqRz/DxW9tIwhb0H02B7JHtod3+\ni7++lWMlQNqpeLYotcoRL4OSX9xuyMtk9vlBLmx+VZMknhgnb411kNRJmV5Yq+U2BJQ7bKtr\n8uptBEgBUk3xfCep/jHxakIcgECf6G72o8mtNYd7qoGdJ4nMOxqxTf/u5GHfYVn/ao4W7+mq\npgKkTXJq+rtlHRmSKmDSUdLxRLB7m25oG6XsSPjY2QIkfIeAoZ3e5QGQ1od3K7IEAdLxcm6J\nUMs4bK0Q8MXZb/Ig5Q7SSDpPb3bYVPbGpZ7cZCTvpGrznjay8JBPWgnS8qU/qJ1jJUB6RPGc\ndRSfYU0Ladouvc4kjDIiO2e0maPRPgWdxgIk56SSQev2dhp2HTWkL3kQSIcBeawESI8ongWp\nBRZJfJd8xCggcXA3LsReApJdfNDgR/MuaSdI2w7DLto68z/MIQVI2+Ts+UgzFtIEKX8q6bDs\niyYukKm28eadvUfKHo0PKzMQWMe3iQjbwoa9OcWxEqPjHFKAtE3OBmm5UKgFkjxNeDJ3WYIz\nexmSLk/pnIQ6R5LzQcUwLecn0ot9IG0iiXh1igNTdutberYF+BPTrQBpj+J6mtuBVBlYmkyM\nSbiDxLGZQCGgubw2IjRDkgUpuysJGvf3draAVK99f8D8jxyNOlYCpEcV17JzNv3N3sbthkUJ\n77LFgBAhLmie7LLMAfAmh1MjHovDTa26iYNB2lYYFCCdKlcAyRDCFOG7sNojvM0Ju8m030nJ\nkNAOfY2apwXJoITYmXFadXVC6f7Ybu2RTwBpQ5MB0ia5AEgWpcoCkfBW8gX5RaIiBVzvQBL/\nUxtrFR7cABLBe1BTbrzbaOWBVPbTQFroIm1pMUDaJJcACTtK1fp+MZP8BCN5RZzxfncuCcEA\nq3d+S9DzgSCJaIvFY3v2R3erDjzeIR1c+nqsBEhHKFaftABSNkSO/tIgUort0MEUtl+AZDiS\nfwj/eBY9SPtRehZIBzqkAGmbXAQkvcnzIMEIpRR/o+9QlMgMuPqMAZUgwSAUdqCw0cNIWnPY\nyQ4pQNom1wGpOZtPJ/q5oX6Swgbo+whHNt/A3Z4aEwoPp9OrINVckqvPO5CkbVOQ8gXaPCk2\nQDpKLgNSJql6t3NZHakBkhVDSSpHZTIUJKr7oxHgYvYaINVI2ueUFg/aw9G87W9tMEDaJNcB\n6cMNHvH9zIlwOx+IcgJv1Ko6cDcCkplrhJwZlwL48TBuHR6qorTPJS19voOjWdvf3GCAtEku\nBNJEUnV6+fQXf8sBJLBkSFOLewFMqiDd10mGsVwazUHGJRHVfdIel7TskXaBNHM3A6TnypVA\nyqvX5MIYB5IjiYO70XZT+F1ONFTpMZzctSpr5SCTQbEV3G1naXayVH5u+5Eg7WjsWXe+dYa6\nFSA9rHhyECQ8GaAAJOK8Qx5Gcj/1MAJU8Swj4jKBRN4RucQFA9VwSXu8UvMA0qz+DpBat3NP\n5uJpt75xiroVIB2imCZ7UJgsR5MNwmwGsh7Bp7lrII0Yy+WZui4nV9SyFqw9i6TppNJSLpvT\nAwHSeXJBkHgHe1utrSVkSFYo8SCV+BTDTGP2fpOuMmyrlRB5wp5BUgY2q93KUfMBLgHSk+XC\nIGGxXTm9TuI3zIqXHqkFgFgdg/Thd7B71zaLFg8giZwvDJACpAcVm8DO2ZowkxHzILnaVKrb\nv5JEH54jT1LjfXvENpCqJHm/aAPcBZBmLuYOkF5sGgHSkxTbuRXG/GRiQ7mCgvgFCY9yYNbM\nt0F05z7bDNIBJBXA02q/dGjOLkDaKNcFyUydNQluoIhzdj5vpyjlHUuXJIOvE0qVPRplds3Y\nbhtJpCc/C9LU7qOG/4wpgkdLgPQ0xRrVYGXQYCpVmSxvomCs2T6dxyHpY6VRWSGklgRvZ/T2\nksRJQD8LvjHk+6AD6SCyC5CeqDiTJAuODCQdI/REHiRkSJjyBT5uVJaTZZi+a2Uq2iCtJkmr\nzBGldiJjwebp2HrVuRGpp0mA9FTFJpug/kXfgbflV938wksejzSSs7/88lr+OGLmek/7SKLK\nEQ13tAhSSuPPX8XNHB1whzdKgPRcxabeW8CxfQwTIlEjx8dxXH02xIfkJoAgLt2b6T3tJMli\nLGfabnXO6ldcxI0cPXRnd0qA9GTFZS9CYrayLzSB5ElattPRjyNhJXmZh5hrad14ku+uLZ7e\n3KM7VlzDAOnZcn2QqiQZD8VUKUitsc6Zn/yPakeII77CsGdIWuGUqnmPWTrbPmnN0yc2zo0N\nkLZLByBZkgQZ4ClPOReQnE9SN9A2VrFkMWhIPJRHzYK06JTK05hvL7e52+o3rNNKZzmkAOkF\nii1JSI9zOAsgzZE0+jhuIdZasvrZeRKL2KwGadVl3FAbka7Bznv6kARIL1AMPaAkMvMcekdc\n8ZBMom7BSzGZYLAYvi3bfROjPRw1QFp3FTc+ESZA2iF9gPRzd07YTS90UexkmtlCYbn5Rmn1\nEkpitSPR/HjRchMHuqPU4LM5Oimm47PUrQDpeYoJJj1MQT8yhFlwiVLUfXlZY7XjrFNa0UYT\npE30mAbLJ6+tvXYrQTqVpADp9YpJU9zeKalNTIZXi7DWoJSBa3aqdoO02yFVgrv1IwgB0tOl\nT5Bu/MxLRkl4Km1jea5C03BnlmlYc/jBIPkc+Poh7es+ggJPU7cCpFcp5pQDTvFrPY6rOqS0\nznAbo0jjqtiuTtIjIN0LHDTAW8/RhmVUAqS90jFIiAl2j7xtrJn1Mw/EuvcqOx0OkiQtnxDX\nBUiPSJ8gQQdaK4Da1vGQPe/2SE8AiRveYvHXXvEbzlO3AqQXKiZ+6piGd/Mgbc7cZaP1tUMM\nyZpjn8TRpjHTjSuoPHhb9kuAdJbiKSM+3Xweom0Zh1vXQalaYbRJUdWaV1h8oXYHN7WGx/Uk\nbS20e/i+7JQA6UzFNo0110tycRbl+rwli81q6pisGJXVQePpJHZhM3dmW6/R/JUKkHbLBa7b\nI424xe/qZTR+6p91TJNVLlhr4/N1Pk2Ghg9zSFOrGy6SH3+aS4ifUx/0ESCdqrhccr/KUVtS\ngNcEQorPmva8yuZn8ui7hbbM6HKPu56P904yiQDpPMWlOZTPWFqeZZejvKqxZj1tc15n9HaC\nxiGyKeEA4wXZ5cy5pJhGsUP6Bqm0gpvrAKxf1qcy6Uj0POSRwPZ3ADPT2uZyReVoFqQD7swO\nCZDOU1wFiYAkWANvhV8qTFUVtSpYt6FxrE/akG/IX2IdSAfcmD0SIJ2nuAaSzrCQMgBa5ZeK\ndRngDJvDT9vI2DaCtdzcxqtFGrhdMG0XIJ2nuFaiat0Tb20K7hoDnnWSthr/kSBtjMLge10v\n1xAgnai4lqOrG4dHphXcLRUNPAzSkSRtyzeYma8B0tHy6UBargCv9ply6LassrDm7eZ/DEap\nsU2XK0B6nvQMkjwdE1fbXwKJ8uMALUAbxmQe5eLgJPjOSxcgHSw9g6Q5AEUq57/d2D0Zx4OH\nbtVYseXNtn8BkiLZcLT0DZJuMRVpfSF6e7N+SkB6VGPNlDcb/xEEsfKd46cztfIPXqK9EiBd\nSvHkkd4mSY4pJcAPA+kIkg70Sbvcav4mxpsHSA/KpwPpTg+DxBvHgVQnabv5HwERNPfQ1wqQ\njpDPBlL6pc0A5X+e7JH2BHfnFgv57xQgPS6fDiQOWrJPynI5kM4scSi+lOEoQNolnxCk1Hzq\nPKQEBNHmNPd8w96Qt5v+oSQ9+LXIyMNXaO9Z6FaAdFXFafDoMK3Fg8f2ruZ9FEs5Y/nAN+KD\nzzPUAKkzxYdI/vV+GKRj03dnX5WHJEDqTPGBoiTtN/+DSeL/+5MAqTPFR4qpF2/LgvkfiNIH\nP+Lo7CuzXQKkzhQfKVtmms+Y/zEgCc+P5sPPkQCpM8WHyi6SPDoHgiRbZ1+Y7RIgdab4QFnt\nTLKbIH5hDzy2ziFAOkUCpAdkQ1Tmu0x45PEgRWj3cgmQHpDt3RuNvdYlI/ZJjxw9BpLvFb7+\nAgRID8iDTw1jig4GqctUw2Mg+eHoE65AgPSIHO9MHpNOM9+TPAAS+QbCI/UmFwKpY4YmOQ4k\nitCuN3lC/2av9E3RR4DUneIj5V5HcDZBSbrnCEH60ZZqkboF6bji5C0SID0m+TkTR69GvBmj\n/i/nSpDah2L9eoDUnaSx1nFyTWfB9AkwOgwkn3h4lQRIDwuXt1HjcbMvAOnsS3CE7AKJZKaZ\n/FsJ/V4hAdIRIrfulAjvc1zKo0K7Yvs1EiAdKwHSXnkApDK/ECD1LmfEdp/jUj4CkpQIYebu\ntRIgHSsp9RAgbZeHQDpfAqSDZco5vBKmz5D6vkuA1JniF8jT83daN/5ZMAqQulP8AslPjnki\nSKjpk0iA1Jnil8nzSPpE+KgESJ0pfp08CaTPE80ZCZA6U/w6ecbk18+SoyslQOpM8Uvl+Jnk\nn/a6BUidKX658GonhwD1OcO6uwRInSk+QXJF6xGjS5/3sgVInSk+Tw4I8z6vQwqQelN8ojxM\n0ifmKEDqTfG58hBIn5mjAKk3xecKdJR2PDHz7LN/pgRInSk+V0yVXIAEEiB1pvh8yQ+2teV4\na/pPn/qaBUidKb6SuOewNII+QexTX7MAqTPFlxLtME2v1DmZx77oB2ef7xMlQOpM8bVkWkR/\nWs5LX/CmMhYgBUiXU3wxyQtKEb/S98FXESD2WSVA6kxxL8LP9+NXn/2CBUidKe5G8mrIZ5/G\nqyRA6kxxR9L7k1o2SYDUmeKu5AtdpQCpM8Uh15QAqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPF\nIdeUAKkzxSHXlACpM8Uh15QAqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPFIdeUAKkzxSHXlACp\nM8Uh15QAqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPFIdeU\nAKkzxSHXlACpM8Uh15QAqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPFIdeUAKkzxSHXlACpM8Uh\n15QAqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPFIdeUAKkz\nxSHXlACpM8Uh15QAqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPFIdeUAKkzxSHXlACpM8Uh15QA\nqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPFIdeUAKkzxSHX\nlACpM8Uh15QAqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPF\nIdeUAKkzxSHXlACpM8Uh15SHQCKixotXSYAUcg15BCTC482Ll0mAFHINeQAkwgbMi9dJgBRy\nDTkWpJdLgBRyDQGQ/l9biKU8VECKPlLIF5aVILUPZZCijxTyleUwkFxrL5IAKeQaEiB1pjjk\nmrILpNRbCpBCQljCI3WmOOSa8gBIMSAbEsLyCEiS8SZ88VIJkEKuIQ+BdL4ESCHXkACpM8Uh\n15QAqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPFIdeUAKkz\nxSHXlACpM8Uh15QAqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPFIdeUAKkzxSHXlACpM8Uh15QA\nqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPFIdeUAKkzxSHX\nlACpM8Uh15QAqTPFIdeUAKkzxSHXlACpM8Uh15QAqTPFIdeUAKkzxSHXlACpM8UhC0JWXqZW\ntwKkHhSHzMtPdgaQAGmdBEghRihA2iUBUogRi9HrOAqQelMc0hby/ihAWisBUoiKhShA2iAB\nUogIBUi7JUAKYalwFCCtlQApRIaOzuQoQOpNcYiXxFCNowBptQRIITWCAqSNEiCFBEgHSIAU\nEiAdIAFSSIB0gARIIW2QXnmTAqTOFId4CZAOkAApJEA6QAKkkADpAAmQQgKkAyRACmmC9NJ7\nFCB1pjjEyTUcUoDUm+IQJwHSERIgfXkJkI6QAOnLS4B0hARIX16ukWsIkHpTHOIkQDpCAqQv\nLwHSERIgfXkJkI6QAOnLS4B0hARIX14CpCMkQPry0gLpxXcoQOpMcYiT6vJBAdJGCZBCGiQF\nSFskQAppBHcB0hYJkELqLunVNyhA6kxxSCkB0sMSIIUESAdIgBQSIB0gAVJIvZMUIG2SACnk\no0pSgLRJAqSQuwRID0qAFDLJeU9h5hPQrQCpB8UhDQmQHpEAKSSLdUkB0jYJkEJYAqQHJEAK\nYQmQHpAAKYSFzuQoQOpNcUhLsJMUIG2UAClEhDQHHiBtlAApBIVJCpA2SoAUYiRA2icBUoiR\n7JI6A+lnTNp48SoJkEKsTCSdaonbQSI83rx4mQRIIU7uJPUFEmED5sXrJEAK8fKTpABpq5wH\nUkiIkW2mYWwJ/v1yoV1IyFHinBB9qWRDSMhREh4pJOQA+dJ9pJCQxyX1lgKkkJADJEAKCTlC\nvvKAbEjIccKJOsIXrz2D16sMCfl8EiCFhBwgAVJIyAESIIWEHCABUkjIARIghYQcIAFSSMgB\nEiCFhBwgAVJIyAESIIWEHCABUkjIARIghYQcIAFSSMgBEiCFhBwgAVJIyAESIIWEHCABUkjI\nARIghYQcIAFSSMgBEiCFhBwgAVJIyAESIIWEHCABUkjIAfL/AwY2H0y5D0+yAAAAAElFTkSu\nQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# spplot(gw.ss.bs$SDF, \"Spearman_rho_E_s11_to_1.Poverty\",key.space = \"right\", \n",
    "#        col.regions = mypalette.3, at = c(-0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1), \n",
    "#        main = \"GW correlations: SABER11 and Poverty (robust)\", \n",
    "#        sp.layout = map.layout)\n",
    "spplot(gw.ss.bs$SDF, SpeRhoDepVarIndVar3,key.space = \"right\", \n",
    "       col.regions = mypalette.3, pretty=TRUE, cuts=8, \n",
    "       main = \"GW correlations: Dependent and third independent variable (robust)\", \n",
    "       sp.layout = map.layout)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the model has been automatically selected through the pseudo stepwise procedure outlined in Figure 1 (and executed in the last two code snipplets), determine the optimal bandwidth for the optimized model. If a strong theoretical model exists, and the last few steps were omitted, replace the corresponding formula on the commented out code snippled at the top."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adaptive bandwidth (number of nearest neighbours): 668 AICc value: 4797.465 \n",
      "Adaptive bandwidth (number of nearest neighbours): 421 AICc value: 4756.648 \n",
      "Adaptive bandwidth (number of nearest neighbours): 266 AICc value: 4755.709 \n",
      "Adaptive bandwidth (number of nearest neighbours): 173 AICc value: 4801.078 \n",
      "Adaptive bandwidth (number of nearest neighbours): 326 AICc value: 4750.123 \n",
      "Adaptive bandwidth (number of nearest neighbours): 361 AICc value: 4750.819 \n",
      "Adaptive bandwidth (number of nearest neighbours): 302 AICc value: 4751.859 \n",
      "Adaptive bandwidth (number of nearest neighbours): 338 AICc value: 4750.386 \n",
      "Adaptive bandwidth (number of nearest neighbours): 315 AICc value: 4750.466 \n",
      "Adaptive bandwidth (number of nearest neighbours): 329 AICc value: 4750.222 \n",
      "Adaptive bandwidth (number of nearest neighbours): 320 AICc value: 4750.076 \n",
      "Adaptive bandwidth (number of nearest neighbours): 320 AICc value: 4750.076 \n",
      "The optimal adaptative bandwidth is equal to  320"
     ]
    }
   ],
   "source": [
    "#bw.gwr.1 <- bw.gwr(whatever_theoretical_model_is_selected, data = PolygonShapeTrimmed, approach = \"AICc\",\n",
    "#                   kernel = \"bisquare\", adaptive = TRUE)\n",
    "bw.gwr.1 <- bw.gwr(as.formula(sorted.models[[1]][[length(sorted.models[[1]])]][[1]]), data = PolygonShapeTrimmed, approach = \"AICc\",\n",
    "                   kernel = \"bisquare\", adaptive = TRUE)\n",
    "cat(\"\")\n",
    "cat(\"The optimal adaptative bandwidth is equal to \", bw.gwr.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the adaptative bandwidth has been determined, and using the theoretical model determined by the pseudo stepwise procedure above, the Geographically Weighted Regressions are executed. As before, ff a strong theoretical model exists, and the last few steps were omitted, replace the corresponding formula on the commented out code snippled at the top.\n",
    "\n",
    "Note that the option adaptative can be changes either to TRUE or to FALSE, with the resulting change in the way the regression is estimated. Also note that the kernel has been specified to be bisquare, but can be changed to be a number of possibilities, including gaussian, boxcar, exponential and tricube. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   ***********************************************************************\n",
       "   *                       Package   GWmodel                             *\n",
       "   ***********************************************************************\n",
       "   Program starts at: 2021-09-06 18:20:38 \n",
       "   Call:\n",
       "   gwr.basic(formula = as.formula(sorted.models[[1]][[length(sorted.models[[1]])]][[1]]), \n",
       "    data = PolygonShapeTrimmed, bw = bw.gwr.1, kernel = \"bisquare\", \n",
       "    adaptive = TRUE, F123.test = TRUE)\n",
       "\n",
       "   Dependent (y) variable:  E_s11_to_1\n",
       "   Independent variables:  Vulnerabil GC_discapi Lack_Respo Threat SISBEN1PC Poverty MurderRate HS_Cober_7 HS_Cober_1 TransEducP GC_indrura ProxyGDP G_IGA_tota TransAlimE\n",
       "   Number of data points: 1070\n",
       "   ***********************************************************************\n",
       "   *                    Results of Global Regression                     *\n",
       "   ***********************************************************************\n",
       "\n",
       "   Call:\n",
       "    lm(formula = formula, data = data)\n",
       "\n",
       "   Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-7.9422 -1.6557 -0.0638  1.4837 10.8037 \n",
       "\n",
       "   Coefficients:\n",
       "                 Estimate Std. Error t value Pr(>|t|)    \n",
       "   (Intercept)  4.564e+01  9.593e-01  47.582  < 2e-16 ***\n",
       "   Vulnerabil  -6.474e-01  8.082e-02  -8.010 3.01e-15 ***\n",
       "   GC_discapi   2.757e-04  1.494e-03   0.185   0.8536    \n",
       "   Lack_Respo  -5.360e-02  7.173e-02  -0.747   0.4551    \n",
       "   Threat       1.207e-01  5.724e-02   2.108   0.0353 *  \n",
       "   SISBEN1PC   -1.773e+01  3.871e+00  -4.580 5.20e-06 ***\n",
       "   Poverty      4.197e-02  7.559e-03   5.552 3.56e-08 ***\n",
       "   MurderRate  -1.156e-02  2.808e-03  -4.116 4.15e-05 ***\n",
       "   HS_Cober_7   5.764e-03  3.870e-03   1.489   0.1367    \n",
       "   HS_Cober_1  -5.864e-03  3.409e-03  -1.720   0.0857 .  \n",
       "   TransEducP   5.273e-07  1.091e-06   0.483   0.6289    \n",
       "   GC_indrura   1.367e+00  4.869e-01   2.806   0.0051 ** \n",
       "   ProxyGDP     3.229e-01  1.293e-01   2.498   0.0127 *  \n",
       "   G_IGA_tota   6.908e-02  9.178e-03   7.527 1.11e-13 ***\n",
       "   TransAlimE  -2.082e-04  4.513e-05  -4.613 4.46e-06 ***\n",
       "\n",
       "   ---Significance stars\n",
       "   Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 \n",
       "   Residual standard error: 2.458 on 1055 degrees of freedom\n",
       "   Multiple R-squared: 0.4969\n",
       "   Adjusted R-squared: 0.4902 \n",
       "   F-statistic: 74.41 on 14 and 1055 DF,  p-value: < 2.2e-16 \n",
       "   ***Extra Diagnostic information\n",
       "   Residual sum of squares: 6375.49\n",
       "   Sigma(hat): 2.443268\n",
       "   AIC:  4978.267\n",
       "   AICc:  4978.784\n",
       "   ***********************************************************************\n",
       "   *          Results of Geographically Weighted Regression              *\n",
       "   ***********************************************************************\n",
       "\n",
       "   *********************Model calibration information*********************\n",
       "   Kernel function: bisquare \n",
       "   Adaptive bandwidth: 320 (number of nearest neighbours)\n",
       "   Regression points: the same locations as observations are used.\n",
       "   Distance metric: Euclidean distance metric is used.\n",
       "\n",
       "   ****************Summary of GWR coefficient estimates:******************\n",
       "                     Min.     1st Qu.      Median     3rd Qu.    Max.\n",
       "   Intercept   4.3266e+01  4.6500e+01  4.9779e+01  5.1864e+01 55.4274\n",
       "   Vulnerabil -1.2679e+00 -7.0055e-01 -6.2121e-01 -3.4209e-01  0.1576\n",
       "   GC_discapi -1.5600e-02 -5.6395e-03 -1.5157e-03  1.8793e-03  0.0105\n",
       "   Lack_Respo -6.4644e-01 -3.9612e-01 -1.9071e-01 -5.5824e-02  0.3122\n",
       "   Threat     -5.1002e-01 -1.9737e-01 -7.2290e-02  1.0971e-01  0.4118\n",
       "   SISBEN1PC  -6.0334e+01 -2.7607e+01 -1.2027e+01 -2.4225e+00 18.5606\n",
       "   Poverty    -1.6183e-02  1.4969e-02  3.0852e-02  5.3203e-02  0.1213\n",
       "   MurderRate -2.4734e-02 -7.7402e-03 -4.4777e-03  1.1908e-03  0.0144\n",
       "   HS_Cober_7 -2.5216e-02 -1.0706e-02 -2.9318e-03  4.7080e-03  0.0230\n",
       "   HS_Cober_1 -2.4721e-02 -1.1182e-02  2.5051e-03  6.5540e-03  0.0147\n",
       "   TransEducP -4.1503e-06 -1.6671e-06  9.4645e-07  3.0749e-06  0.0000\n",
       "   GC_indrura -4.8945e+00 -9.8918e-01  5.5423e-01  1.3441e+00  3.7252\n",
       "   ProxyGDP   -5.6169e-01 -7.6080e-02  1.0505e-01  4.1088e-01  0.9605\n",
       "   G_IGA_tota -1.3054e-02  2.9568e-02  4.6363e-02  6.6074e-02  0.0919\n",
       "   TransAlimE -4.1943e-04 -2.7758e-04 -2.0194e-04 -1.0261e-04  0.0001\n",
       "   ************************Diagnostic information*************************\n",
       "   Number of data points: 1070 \n",
       "   Effective number of parameters (2trace(S) - trace(S'S)): 162.0735 \n",
       "   Effective degrees of freedom (n-2trace(S) + trace(S'S)): 907.9265 \n",
       "   AICc (GWR book, Fotheringham, et al. 2002, p. 61, eq 2.33): 4750.076 \n",
       "   AIC (GWR book, Fotheringham, et al. 2002,GWR p. 96, eq. 4.22): 4590.117 \n",
       "   Residual sum of squares: 4068.89 \n",
       "   R-square value:  0.6788869 \n",
       "   Adjusted R-square value:  0.621502 \n",
       "   ******************F test results of GWR calibration********************\n",
       "   ---F1 test (Leung et al. 2000)\n",
       "    F1 statistic Numerator DF Denominator DF     Pr(>)    \n",
       "         0.74159          Inf           1055 2.239e-13 ***\n",
       "   ---F2 test (Leung et al. 2000)\n",
       "    F2 statistic Numerator DF Denominator DF Pr(>)\n",
       "          2.5952     -28.4294           1055    NA\n",
       "   ---F3 test (Leung et al. 2000)\n",
       "              F3 statistic Numerator DF Denominator DF     Pr(>)    \n",
       "   Intercept       2.31727    397.17500            Inf < 2.2e-16 ***\n",
       "   Vulnerabil      2.63093    434.67253            Inf < 2.2e-16 ***\n",
       "   GC_discapi      2.60891    349.84095            Inf < 2.2e-16 ***\n",
       "   Lack_Respo      1.61818    316.18672            Inf 2.001e-11 ***\n",
       "   Threat          2.27177    334.59684            Inf < 2.2e-16 ***\n",
       "   SISBEN1PC       3.62863    368.89562            Inf < 2.2e-16 ***\n",
       "   Poverty         2.80257    400.92334            Inf < 2.2e-16 ***\n",
       "   MurderRate      0.92235    126.41160            Inf    0.7230    \n",
       "   HS_Cober_7      1.78191    193.01631            Inf 1.390e-10 ***\n",
       "   HS_Cober_1      2.12237    277.85263            Inf < 2.2e-16 ***\n",
       "   TransEducP      1.33958     83.28561            Inf    0.0210 *  \n",
       "   GC_indrura      2.54794    308.48039            Inf < 2.2e-16 ***\n",
       "   ProxyGDP        1.02305    111.16572            Inf    0.4148    \n",
       "   G_IGA_tota      1.69754    396.39560            Inf < 2.2e-16 ***\n",
       "   TransAlimE      0.85819    224.95483            Inf    0.9394    \n",
       "   ---F4 test (GWR book p92)\n",
       "    F4 statistic Numerator DF Denominator DF     Pr(>)    \n",
       "         0.63821    907.92654           1055 1.906e-12 ***\n",
       "\n",
       "   ---Significance stars\n",
       "   Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 \n",
       "   ***********************************************************************\n",
       "   Program stops at: 2021-09-06 18:26:52 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#gwr.res <- gwr.basic(whatever_theoretical_model_is_selected, data = PolygonShapeTrimmed, bw = bw.gwr.1, \n",
    "#                     kernel = \"bisquare\", adaptive = TRUE, F123.test = TRUE)\n",
    "\n",
    "gwr.res <- gwr.basic(as.formula(sorted.models[[1]][[length(sorted.models[[1]])]][[1]]), data = PolygonShapeTrimmed, bw = bw.gwr.1, \n",
    "                     kernel = \"bisquare\", adaptive = TRUE, F123.test = TRUE)\n",
    "gwr.res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The names of the results are printed out, in case something wants to be produced with it. These same variables will be exported at the end of the code to a Shapefile, to be plotted and formatted on QGIS. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'Intercept'</li><li>'Vulnerabil'</li><li>'GC_discapi'</li><li>'Lack_Respo'</li><li>'Threat'</li><li>'SISBEN1PC'</li><li>'Poverty'</li><li>'MurderRate'</li><li>'HS_Cober_7'</li><li>'HS_Cober_1'</li><li>'TransEducP'</li><li>'GC_indrura'</li><li>'ProxyGDP'</li><li>'G_IGA_tota'</li><li>'TransAlimE'</li><li>'y'</li><li>'yhat'</li><li>'residual'</li><li>'CV_Score'</li><li>'Stud_residual'</li><li>'Intercept_SE'</li><li>'Vulnerabil_SE'</li><li>'GC_discapi_SE'</li><li>'Lack_Respo_SE'</li><li>'Threat_SE'</li><li>'SISBEN1PC_SE'</li><li>'Poverty_SE'</li><li>'MurderRate_SE'</li><li>'HS_Cober_7_SE'</li><li>'HS_Cober_1_SE'</li><li>'TransEducP_SE'</li><li>'GC_indrura_SE'</li><li>'ProxyGDP_SE'</li><li>'G_IGA_tota_SE'</li><li>'TransAlimE_SE'</li><li>'Intercept_TV'</li><li>'Vulnerabil_TV'</li><li>'GC_discapi_TV'</li><li>'Lack_Respo_TV'</li><li>'Threat_TV'</li><li>'SISBEN1PC_TV'</li><li>'Poverty_TV'</li><li>'MurderRate_TV'</li><li>'HS_Cober_7_TV'</li><li>'HS_Cober_1_TV'</li><li>'TransEducP_TV'</li><li>'GC_indrura_TV'</li><li>'ProxyGDP_TV'</li><li>'G_IGA_tota_TV'</li><li>'TransAlimE_TV'</li><li>'Local_R2'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Intercept'\n",
       "\\item 'Vulnerabil'\n",
       "\\item 'GC\\_discapi'\n",
       "\\item 'Lack\\_Respo'\n",
       "\\item 'Threat'\n",
       "\\item 'SISBEN1PC'\n",
       "\\item 'Poverty'\n",
       "\\item 'MurderRate'\n",
       "\\item 'HS\\_Cober\\_7'\n",
       "\\item 'HS\\_Cober\\_1'\n",
       "\\item 'TransEducP'\n",
       "\\item 'GC\\_indrura'\n",
       "\\item 'ProxyGDP'\n",
       "\\item 'G\\_IGA\\_tota'\n",
       "\\item 'TransAlimE'\n",
       "\\item 'y'\n",
       "\\item 'yhat'\n",
       "\\item 'residual'\n",
       "\\item 'CV\\_Score'\n",
       "\\item 'Stud\\_residual'\n",
       "\\item 'Intercept\\_SE'\n",
       "\\item 'Vulnerabil\\_SE'\n",
       "\\item 'GC\\_discapi\\_SE'\n",
       "\\item 'Lack\\_Respo\\_SE'\n",
       "\\item 'Threat\\_SE'\n",
       "\\item 'SISBEN1PC\\_SE'\n",
       "\\item 'Poverty\\_SE'\n",
       "\\item 'MurderRate\\_SE'\n",
       "\\item 'HS\\_Cober\\_7\\_SE'\n",
       "\\item 'HS\\_Cober\\_1\\_SE'\n",
       "\\item 'TransEducP\\_SE'\n",
       "\\item 'GC\\_indrura\\_SE'\n",
       "\\item 'ProxyGDP\\_SE'\n",
       "\\item 'G\\_IGA\\_tota\\_SE'\n",
       "\\item 'TransAlimE\\_SE'\n",
       "\\item 'Intercept\\_TV'\n",
       "\\item 'Vulnerabil\\_TV'\n",
       "\\item 'GC\\_discapi\\_TV'\n",
       "\\item 'Lack\\_Respo\\_TV'\n",
       "\\item 'Threat\\_TV'\n",
       "\\item 'SISBEN1PC\\_TV'\n",
       "\\item 'Poverty\\_TV'\n",
       "\\item 'MurderRate\\_TV'\n",
       "\\item 'HS\\_Cober\\_7\\_TV'\n",
       "\\item 'HS\\_Cober\\_1\\_TV'\n",
       "\\item 'TransEducP\\_TV'\n",
       "\\item 'GC\\_indrura\\_TV'\n",
       "\\item 'ProxyGDP\\_TV'\n",
       "\\item 'G\\_IGA\\_tota\\_TV'\n",
       "\\item 'TransAlimE\\_TV'\n",
       "\\item 'Local\\_R2'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Intercept'\n",
       "2. 'Vulnerabil'\n",
       "3. 'GC_discapi'\n",
       "4. 'Lack_Respo'\n",
       "5. 'Threat'\n",
       "6. 'SISBEN1PC'\n",
       "7. 'Poverty'\n",
       "8. 'MurderRate'\n",
       "9. 'HS_Cober_7'\n",
       "10. 'HS_Cober_1'\n",
       "11. 'TransEducP'\n",
       "12. 'GC_indrura'\n",
       "13. 'ProxyGDP'\n",
       "14. 'G_IGA_tota'\n",
       "15. 'TransAlimE'\n",
       "16. 'y'\n",
       "17. 'yhat'\n",
       "18. 'residual'\n",
       "19. 'CV_Score'\n",
       "20. 'Stud_residual'\n",
       "21. 'Intercept_SE'\n",
       "22. 'Vulnerabil_SE'\n",
       "23. 'GC_discapi_SE'\n",
       "24. 'Lack_Respo_SE'\n",
       "25. 'Threat_SE'\n",
       "26. 'SISBEN1PC_SE'\n",
       "27. 'Poverty_SE'\n",
       "28. 'MurderRate_SE'\n",
       "29. 'HS_Cober_7_SE'\n",
       "30. 'HS_Cober_1_SE'\n",
       "31. 'TransEducP_SE'\n",
       "32. 'GC_indrura_SE'\n",
       "33. 'ProxyGDP_SE'\n",
       "34. 'G_IGA_tota_SE'\n",
       "35. 'TransAlimE_SE'\n",
       "36. 'Intercept_TV'\n",
       "37. 'Vulnerabil_TV'\n",
       "38. 'GC_discapi_TV'\n",
       "39. 'Lack_Respo_TV'\n",
       "40. 'Threat_TV'\n",
       "41. 'SISBEN1PC_TV'\n",
       "42. 'Poverty_TV'\n",
       "43. 'MurderRate_TV'\n",
       "44. 'HS_Cober_7_TV'\n",
       "45. 'HS_Cober_1_TV'\n",
       "46. 'TransEducP_TV'\n",
       "47. 'GC_indrura_TV'\n",
       "48. 'ProxyGDP_TV'\n",
       "49. 'G_IGA_tota_TV'\n",
       "50. 'TransAlimE_TV'\n",
       "51. 'Local_R2'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"Intercept\"     \"Vulnerabil\"    \"GC_discapi\"    \"Lack_Respo\"   \n",
       " [5] \"Threat\"        \"SISBEN1PC\"     \"Poverty\"       \"MurderRate\"   \n",
       " [9] \"HS_Cober_7\"    \"HS_Cober_1\"    \"TransEducP\"    \"GC_indrura\"   \n",
       "[13] \"ProxyGDP\"      \"G_IGA_tota\"    \"TransAlimE\"    \"y\"            \n",
       "[17] \"yhat\"          \"residual\"      \"CV_Score\"      \"Stud_residual\"\n",
       "[21] \"Intercept_SE\"  \"Vulnerabil_SE\" \"GC_discapi_SE\" \"Lack_Respo_SE\"\n",
       "[25] \"Threat_SE\"     \"SISBEN1PC_SE\"  \"Poverty_SE\"    \"MurderRate_SE\"\n",
       "[29] \"HS_Cober_7_SE\" \"HS_Cober_1_SE\" \"TransEducP_SE\" \"GC_indrura_SE\"\n",
       "[33] \"ProxyGDP_SE\"   \"G_IGA_tota_SE\" \"TransAlimE_SE\" \"Intercept_TV\" \n",
       "[37] \"Vulnerabil_TV\" \"GC_discapi_TV\" \"Lack_Respo_TV\" \"Threat_TV\"    \n",
       "[41] \"SISBEN1PC_TV\"  \"Poverty_TV\"    \"MurderRate_TV\" \"HS_Cober_7_TV\"\n",
       "[45] \"HS_Cober_1_TV\" \"TransEducP_TV\" \"GC_indrura_TV\" \"ProxyGDP_TV\"  \n",
       "[49] \"G_IGA_tota_TV\" \"TransAlimE_TV\" \"Local_R2\"     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "writeOGR(gwr.res$SDF, \"Data\\\\Colombia\", driver=\"ESRI Shapefile\", layer='Results', overwrite_layer = TRUE)\n",
    "names(gwr.res$SDF) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A map of the coefficient estimates for the basic GWR estimation for the first independent variable is presented. An customizable example can be found for the first independent variable. The same thing is done for the other two independent variables in the example provided. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6TVqwgS2dAO6PIgndpiniAJUg9Kx7BuIMl0tkYe6QP3kaQdOCQGCaZFEZIgaVda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tpJQXkwSpJ6Upp5WngLSzkSVIKYfLXSCV\n0Vx4Aqr+3U8SpJTD5QkeaXcTS5BSDpcHQJrokN1XEqSUw+URkKQqiOBDkCd/tiRIKYfLQyCd\nRBKklMMlQepJacppJUHqSWnKaSVB6klpymklQepJacppJUHqSWnKaSVB6klpymklQepJacpp\nJUHqSWnKaSVB6klpymklQepJacppJUHqSWnKaSVB6klpymklQepJacppJUHqSWnKaSVB6klp\nymklQepJacppJUHqSWnKaSVB6klpymklQepJacppJUHqSWnKaSVB6klpymklQepJacppJUHq\nSWnKaSVB6klpymklQepJacppJUHqSWnKaSVB6klpymklQepJacppJUHqSWnKaSVB6klpymkl\nQepJacppJUHqSWnKaSVB6klpymklQepJacppJUHqSWnKaSVB6klpymklQepJacppJUHqSWnK\naSVB6klpymklQepJacppJUHqSWnKaSVB6klpymklQepJacppJUHqSWnKaSVB6klpymklQepJ\nacppJUHqSWnKaSVB6klpymklQepJacppJUHqSWnKaSVB6klpymklQepJacppJUHqSWnKaSVB\n6klpymklQepJacppJUHqSWnKaSVB6klpymklQepJacppJUHqSWnKaSVB6klpymklQepJacpp\nJUHqSWnKaSVB6klpymklQepJacppJUHqSWnKaSVB6klpymklQepJacppBUD6vwk5tdkkSCmH\nS4LUk9KU00qC1JPSlNNKgtST0pTTSoLUk9KU00qC1JPSlNNKgtST0pTTSoLUk9KU00qC1JPS\nlNNKgtST0pTTSoLUk9KU00qC1JPSlNNKgtST0pTTSoLUk9KU00qC1JPSlNNKgtST0pTTSoLU\nk9KU00qC1JPSlNNKgtST0pTTSoLUk9KU00qC1JPSlAVCRnbTqksJ0umVpszKD3beQBKkFZIg\npbBQgnS/JEgpIgaj/ThKkLpSmjIp5P1RgrRKEqSUQRxFCdJKSZBSblJzlCCtkgQp5SPkKEFa\nJQnSiwv3GR3IUYLUldKUSAaEIn+UIK2TBOm1JUIoQbpDEqTXlgRpI0mQXlsSpI0kQXptSZA2\nkgTptaUJ0p7vKEHqSWlKIKdwSAlSV0pTAkmQtpIE6aUlQdpKEqSXlgRpK0mQXlpOkWtIkLpS\nmhJIgrSVJEgvLQnSVpIgvbQkSFtJgvTSkiBtJQnSS0sLpH3fUILUk9KUQBKkrSRBemU5R2SX\nIHWlNKWWBGkzSZBeWRKkzSRBemVJkDaTBOmV5Ry5hgSpK6UptYTTByVI90iC9NLSIClBWi0J\n0mtLTFKCtFoSpBeXBGkbSZBeXBKkbSRBenFJkLaRBOnF5djfoSjXoEsJ0umVpsSSIG0iCdLL\ny6G/6DJegS4lSKdXmtKQI3/0slyBLiVIp1ea0hL/O2O7v54EqSelKROSID0qCVLKR4L0uCRI\nKR8J0uOSIKV8JEiPS4KU8mFB2v/tJEg9KU2ZEEzbJUj3SIKUchNIgSdI90iClMJCCdL9kiCl\niFCCdLckSCkiXYL0IyYNPpi1e0iClKJyaycd8HIeAYnwePlg1u4iCVIKyA+S+gKJ8ATygWDl\nTpIgpaDsHhINSnVpG5DcWfeQY0BKSUFZZxnGlOBf9+EFQEpJ2UhaINFLJBtSUjaS1w7tUlI2\nkgmQ9rXtBCmlTxlbSwlSSsoGMpH+TpBSUhZL2CFLL9Ihm5KymXB+zibrMmuXktKjJEgpKRtI\ngpSSsoEkSCkpG0iClJKygSRIKSkbSIKUkrKBJEgpKRtIgpSSsoEkSCkpG0iClJKygSRIKSkb\nSIKUkrKBJEgpKRtIgpSSsoEkSCkpG0iClJKygSRIKSkbSIKUkrKBJEgpKRtIgpSSsoEkSCkp\nG0iClJKygfw/BtPYbAag0bUAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#spplot(gwr.res$SDF, MurderRate, key.space = \"right\", \n",
    "#       col.regions = mypalette.2, pretty=TRUE, cuts=8,\n",
    "#       main = \"Basic GW regression coefficient estimates for murder rates\",\n",
    "#       sp.layout = map.layout)\n",
    "spplot(gwr.res$SDF, ListVariables[[2]], key.space = \"right\", \n",
    "       col.regions = mypalette.2, pretty=TRUE, cuts=8,\n",
    "       main = \"Basic GW regression coefficient estimates for the first independent variable\",\n",
    "       sp.layout = map.layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ktgB0ttxSC9MkiYbTBpBdNJCkGqK1f1HZx9VPixLyRI46MEaR/Dk92JMZaDHAOn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R3XOUIPVmeFuV+0xsvRrxaoz6P50JUmeG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\noAQpldpACVIqtYH+Hw0YuJPbw4wMAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "spplot(gwr.res$SDF, ListVariables[[3]], key.space = \"right\", \n",
    "       col.regions = mypalette.2, pretty=TRUE, cuts=8,\n",
    "       main = \"Basic GW regression coefficient estimates for the second independent variable\",\n",
    "       sp.layout = map.layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cVzsluVG75yAXwjSDIYiMxhml+uD1X/IAWXraokWNeOOBpW3T5pMEmaXV8OFhb/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\nQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "spplot(gwr.res$SDF, ListVariables[[4]], key.space = \"right\", \n",
    "       col.regions = mypalette.2, pretty=TRUE, cuts=8,\n",
    "       main = \"Basic GW regression coefficient estimates for the third independent variable\",\n",
    "       sp.layout = map.layout)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same GWR regression, now calculating robust estimators, is performed. If the user has a strong model framework, it can be added in the commented out code snipplet. Note that his estimation is computationally expensive and can take a long time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   ***********************************************************************\n",
       "   *                       Package   GWmodel                             *\n",
       "   ***********************************************************************\n",
       "   Program starts at: 2021-09-06 20:13:27 \n",
       "   Call:\n",
       "   gwr.basic(formula = formula, data = data, bw = bw, kernel = kernel, \n",
       "    adaptive = adaptive, p = p, theta = theta, longlat = longlat, \n",
       "    dMat = dMat, F123.test = F123.test, cv = T, W.vect = W.vect)\n",
       "\n",
       "   Dependent (y) variable:  E_s11_to_1\n",
       "   Independent variables:  Vulnerabil GC_discapi Lack_Respo Threat SISBEN1PC Poverty MurderRate HS_Cober_7 HS_Cober_1 TransEducP GC_indrura ProxyGDP G_IGA_tota TransAlimE\n",
       "   Number of data points: 1070\n",
       "   ***********************************************************************\n",
       "   *                    Results of Global Regression                     *\n",
       "   ***********************************************************************\n",
       "\n",
       "   Call:\n",
       "    lm(formula = formula, data = data)\n",
       "\n",
       "   Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-7.9422 -1.6557 -0.0638  1.4837 10.8037 \n",
       "\n",
       "   Coefficients:\n",
       "                 Estimate Std. Error t value Pr(>|t|)    \n",
       "   (Intercept)  4.564e+01  9.593e-01  47.582  < 2e-16 ***\n",
       "   Vulnerabil  -6.474e-01  8.082e-02  -8.010 3.01e-15 ***\n",
       "   GC_discapi   2.757e-04  1.494e-03   0.185   0.8536    \n",
       "   Lack_Respo  -5.360e-02  7.173e-02  -0.747   0.4551    \n",
       "   Threat       1.207e-01  5.724e-02   2.108   0.0353 *  \n",
       "   SISBEN1PC   -1.773e+01  3.871e+00  -4.580 5.20e-06 ***\n",
       "   Poverty      4.197e-02  7.559e-03   5.552 3.56e-08 ***\n",
       "   MurderRate  -1.156e-02  2.808e-03  -4.116 4.15e-05 ***\n",
       "   HS_Cober_7   5.764e-03  3.870e-03   1.489   0.1367    \n",
       "   HS_Cober_1  -5.864e-03  3.409e-03  -1.720   0.0857 .  \n",
       "   TransEducP   5.273e-07  1.091e-06   0.483   0.6289    \n",
       "   GC_indrura   1.367e+00  4.869e-01   2.806   0.0051 ** \n",
       "   ProxyGDP     3.229e-01  1.293e-01   2.498   0.0127 *  \n",
       "   G_IGA_tota   6.908e-02  9.178e-03   7.527 1.11e-13 ***\n",
       "   TransAlimE  -2.082e-04  4.513e-05  -4.613 4.46e-06 ***\n",
       "\n",
       "   ---Significance stars\n",
       "   Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 \n",
       "   Residual standard error: 2.458 on 1055 degrees of freedom\n",
       "   Multiple R-squared: 0.4969\n",
       "   Adjusted R-squared: 0.4902 \n",
       "   F-statistic: 74.41 on 14 and 1055 DF,  p-value: < 2.2e-16 \n",
       "   ***Extra Diagnostic information\n",
       "   Residual sum of squares: 6375.49\n",
       "   Sigma(hat): 2.443268\n",
       "   AIC:  4978.267\n",
       "   AICc:  4978.784\n",
       "   ***********************************************************************\n",
       "   *          Results of Geographically Weighted Regression              *\n",
       "   ***********************************************************************\n",
       "\n",
       "   *********************Model calibration information*********************\n",
       "   Kernel function: bisquare \n",
       "   Adaptive bandwidth: 320 (number of nearest neighbours)\n",
       "   Regression points: the same locations as observations are used.\n",
       "   Distance metric: Euclidean distance metric is used.\n",
       "\n",
       "   ****************Summary of GWR coefficient estimates:******************\n",
       "                     Min.     1st Qu.      Median     3rd Qu.    Max.\n",
       "   Intercept   4.3146e+01  4.5923e+01  5.0403e+01  5.2217e+01 55.7235\n",
       "   Vulnerabil -1.5244e+00 -7.6158e-01 -6.0911e-01 -4.2352e-01  0.0981\n",
       "   GC_discapi -1.5938e-02 -4.6688e-03 -1.2568e-03  1.7795e-03  0.0094\n",
       "   Lack_Respo -7.1321e-01 -3.7427e-01 -1.3021e-01  4.0069e-02  0.3235\n",
       "   Threat     -4.9761e-01 -2.3810e-01 -1.1291e-01  1.6087e-01  0.4252\n",
       "   SISBEN1PC  -4.8869e+01 -2.8770e+01 -1.2090e+01 -2.6784e+00 17.0443\n",
       "   Poverty    -2.5362e-02  1.6384e-02  3.1493e-02  5.4599e-02  0.0978\n",
       "   MurderRate -1.9630e-02 -7.8267e-03 -2.5096e-03  2.3295e-03  0.0136\n",
       "   HS_Cober_7 -2.8729e-02 -1.3438e-02 -5.4593e-03  5.1613e-03  0.0246\n",
       "   HS_Cober_1 -2.4600e-02 -1.2217e-02  3.8951e-03  6.7733e-03  0.0136\n",
       "   TransEducP -4.0993e-06 -1.7240e-06  1.1871e-06  4.0661e-06  0.0000\n",
       "   GC_indrura -5.0040e+00 -9.0903e-01 -1.7180e-02  7.9474e-01  3.2423\n",
       "   ProxyGDP   -5.9015e-01 -2.9175e-01  3.4692e-02  3.7913e-01  0.9195\n",
       "   G_IGA_tota -7.6077e-03  3.0166e-02  4.4112e-02  6.1225e-02  0.1100\n",
       "   TransAlimE -3.8570e-04 -2.6937e-04 -1.9651e-04 -7.7077e-05  0.0001\n",
       "   ************************Diagnostic information*************************\n",
       "   Number of data points: 1070 \n",
       "   Effective number of parameters (2trace(S) - trace(S'S)): 159.8235 \n",
       "   Effective degrees of freedom (n-2trace(S) + trace(S'S)): 910.1765 \n",
       "   AICc (GWR book, Fotheringham, et al. 2002, p. 61, eq 2.33): 4798.281 \n",
       "   AIC (GWR book, Fotheringham, et al. 2002,GWR p. 96, eq. 4.22): 4638.556 \n",
       "   Residual sum of squares: 4257.917 \n",
       "   R-square value:  0.6639691 \n",
       "   Adjusted R-square value:  0.6048985 \n",
       "   ******************F test results of GWR calibration********************\n",
       "   ---F1 test (Leung et al. 2000)\n",
       "    F1 statistic Numerator DF Denominator DF     Pr(>)    \n",
       "         0.77412          Inf           1055 3.722e-10 ***\n",
       "   ---F2 test (Leung et al. 2000)\n",
       "    F2 statistic Numerator DF Denominator DF Pr(>)\n",
       "          2.4196     -27.4041           1055    NA\n",
       "   ---F3 test (Leung et al. 2000)\n",
       "              F3 statistic Numerator DF Denominator DF     Pr(>)    \n",
       "   Intercept        2.5011     397.1750            Inf < 2.2e-16 ***\n",
       "   Vulnerabil       3.3166     434.6725            Inf < 2.2e-16 ***\n",
       "   GC_discapi       2.6125     349.8410            Inf < 2.2e-16 ***\n",
       "   Lack_Respo       2.4409     316.1867            Inf < 2.2e-16 ***\n",
       "   Threat           2.4866     334.5968            Inf < 2.2e-16 ***\n",
       "   SISBEN1PC        3.0520     368.8956            Inf < 2.2e-16 ***\n",
       "   Poverty          2.5636     400.9233            Inf < 2.2e-16 ***\n",
       "   MurderRate       1.3368     126.4116            Inf 0.0068291 ** \n",
       "   HS_Cober_7       2.0497     193.0163            Inf  4.62e-16 ***\n",
       "   HS_Cober_1       2.2588     277.8526            Inf < 2.2e-16 ***\n",
       "   TransEducP       1.6790      83.2856            Inf 0.0001041 ***\n",
       "   GC_indrura       2.1916     308.4804            Inf < 2.2e-16 ***\n",
       "   ProxyGDP         1.4369     111.1657            Inf 0.0017537 ** \n",
       "   G_IGA_tota       1.8050     396.3956            Inf < 2.2e-16 ***\n",
       "   TransAlimE       1.0996     224.9548            Inf 0.1457687    \n",
       "   ---F4 test (GWR book p92)\n",
       "    F4 statistic Numerator DF Denominator DF     Pr(>)    \n",
       "         0.66786    910.17652           1055 1.991e-10 ***\n",
       "\n",
       "   ---Significance stars\n",
       "   Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 \n",
       "   ***********************************************************************\n",
       "   Program stops at: 2021-09-06 20:19:39 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rgwr.res <- gwr.robust(as.formula(sorted.models[[1]][[length(sorted.models[[1]])]][[1]]), data = PolygonShapeTrimmed, bw = bw.gwr.1,\n",
    "#                       kernel = \"bisquare\", adaptive = TRUE, F123.test = TRUE)\n",
    "rgwr.res <- gwr.robust(as.formula(sorted.models[[1]][[length(sorted.models[[1]])]][[1]]), data = PolygonShapeTrimmed, bw = bw.gwr.1,\n",
    "                       kernel = \"bisquare\", adaptive = TRUE, F123.test = TRUE)\n",
    "rgwr.res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same maps presented above, but now for the robust version of the estimation, are computed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TWK05ZfbEdJkPrQt6b8tO63tyZL3F10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\nUkrKCpIgpaSsIAlSSsoKkiClpKwg/x/8w5Tbv4wljAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "spplot(rgwr.res$SDF, ListVariables[[2]], key.space = \"right\", \n",
    "       col.regions = mypalette.1, pretty=TRUE, cuts=8,\n",
    "       main = \"Robust GW regression coefficient estimates for the first independent variable\",\n",
    "       sp.layout = map.layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sF0kqoglZWr+gnOPspAyRhSgBQgHW54tjuR+0WKEPSX1oBkq4GwM1OChHLVqy5X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f41QGSIMZfqwCpFsVIA1m+LE6Irb7HKcy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\nTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "spplot(rgwr.res$SDF, ListVariables[[3]], key.space = \"right\", \n",
    "       col.regions = mypalette.2, pretty=TRUE, cuts=8,\n",
    "       main = \"Robust GW regression coefficient estimates for the second independent variable\",\n",
    "       sp.layout = map.layout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oxPGSQ6TCpQUYxnrGm4hS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\nB1PNS8KV+WUGAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "spplot(rgwr.res$SDF, ListVariables[[4]], key.space = \"right\", \n",
    "       col.regions = mypalette.3, pretty=TRUE, cuts=8,\n",
    "       main = \"Robust GW regression coefficient estimates for the third independent variable\",\n",
    "       sp.layout = map.layout)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the results from the estimations are exported as an ESRI Shapefile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "writeOGR(rgwr.res$SDF, \"Data\\\\Colombia\", driver=\"ESRI Shapefile\", layer='Results - Robust', overwrite_layer = TRUE)"
   ]
  },
  {
   "attachments": {
    "Joining%20the%20residuals.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### These are the instructions to produce Figure 5\n",
    "\n",
    "- Load the CSV \"ModelOLS.csv\" onto QGIS\n",
    "- Load the shapefile of Colombia produced above (\"Results - Robust\")\n",
    "- Go to the shapefile Properties, and then to Joins.\n",
    "- Create a new Join, using the following parameters:\n",
    "    - Join layer: ModelOLS CSV (shown in the image below as Database_final)\n",
    "    - Join field: admin2Pcod (this is a unique identifier for each municipality in Colombia)\n",
    "    - Target field: admin2Pcod\n",
    "    - Cache join layer in memory: Yes\n",
    "    - Joined fields: Yes\n",
    "        - Select only Residuals\n",
    "    - Custom field name prefix: Yes, leave as blank.\n",
    "\n",
    "![Joining%20the%20residuals.PNG](attachment:Joining%20the%20residuals.PNG)\n",
    "\n",
    "Once this is done, the user can go to the Layer Styling panel, and choose the variable Residuals. Once selected, the user must select 5-class quintile mode. "
   ]
  },
  {
   "attachments": {
    "Replicating%20maps.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This shapefile contains the information necessary to produce all the following figures\n",
    "\n",
    "Note: The variable presented for statistical significance represents the alpha, so observations with an alpha in absolute values below 1.959 is considered statistically non significant. \n",
    "\n",
    "- Figure 6: variable \"Thret\" for the coefficients, variable \"Th_TV\" for statistical significance\n",
    "- Figure 7: variable \"Lc_R2\"\n",
    "- Figure 9: \n",
    "    - Vulnerability index: variable \"Vlnrb\" for the coefficients, variable \"Vl_TV\" for statistical significance\n",
    "    - Distance to department capital: variable \"GC_ds\" for the coefficients, variable \"GC_n_TV\" for statistical significance\n",
    "    - Lack of response capacity: variable \"Lck_R\" for the coefficients, variable \"L_R_T\" for statistical significance\n",
    "    - Thread index: variable \"Thret\" for the coefficients, variable \"Th_TV\" for statistical significance\n",
    "    - Proportion of SISBEN1: variable \"SISBEN1PC\" for the coefficients, variable \"SISBEN1P_1\" for statistical significance\n",
    "    - Poverty: variable \"Pvrty\" for the coefficients, variable \"Pv_TV\" for statistical significance\n",
    "    - Murder rate: variable \"MrdrR\" for the coefficients, variable \"MR_TV\" for statistical significance\n",
    "    - Connection to sewage: variable \"HS_Cb_7\" for the coefficients, variable \"HS_C_7_T\" for statistical significance\n",
    "    - Connection to water system: variable \"HS_Cb_1\" for the coefficients, variable \"HS_C_1_T\" for statistical significance\n",
    "    - Transfers to education: variable \"TrnEP\" for the coefficients, variable \"TEP_T\" for statistical significance\n",
    "    - Rurality index: variable \"GC_nd\" for the coefficients, variable \"GC_n_TV\" for statistical significance\n",
    "    - GDP proxy: variable \"PrGDP\" for the coefficients, variable \"PGDP_T\" for statistical significance\n",
    "    - Transfers to school feeding programs: variable \"TrnAE\" for the coefficients, variable \"TAE_T\" for statistical significance\n",
    "    - Open Government index: variable \"G_IGA_t\" for the coefficients, variable \"G_IGA__T\" for statistical significance\n",
    "\n",
    "To create Figure 5 and Figure 7, a 5-class quintile mode was used, using the Graduated functionality of the Layer Styling panel. For all other figures listed above, the method was as follows: \n",
    "\n",
    "- The variable was first split using a 5-class quintile mode, using the Graduated functionality of the Layer Styling panel.\n",
    "- Changing the Graduated functionality to a Rule-based one on the Layer Styling panel.\n",
    "- Adding an additional rule stating that if the alpha value of each observation was below 1.959 in absolute value the observation was to be shaded and labeled as non-statistically significant.\n",
    "- The Symbol levels were established so as to render the statistical significance layer on top of the others, under the Advanced option of the Layer Styling panel. \n",
    "\n",
    "A view of the final result can be seen below.\n",
    "\n",
    "![Replicating%20maps.PNG](attachment:Replicating%20maps.PNG)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "End of the code."
   ]
  }
 ],
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   "file_extension": ".r",
   "mimetype": "text/x-r-source",
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