RepData_PeerAssessment2/StormData.html at master · pmoosh/RepData_PeerAssessment2 · GitHub

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<h1>Impact Analysis: How Severe Weather Events impact the United States </h1>

<h2>Synopsis</h2>

<p>This analysis hopes to find some answers to the following questions:<br/>
1. What weather events are most harmful with respect to poulation health?<br/>
2. Which events have the greatest ecomomic consequences?  </p>

<p>These first observations show that tornados are the largest cause of lifes<br/>
whereas floods generate the biggest finanicial losses</p>

<h2>Loading and Processing the Raw Data</h2>

<p>The data can be downloaded from <a href="https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2">Storm Data</a>. Please uncompress the data with a utility such as   bunzip2 or gunzip. If you wish to rerun these calculations, please use setwd() to set the   working directory to the directory where &ldquo;StormData.csv&rdquo; is located. </p>

<pre><code class="r">storm_table &lt;- read.csv(&quot;./StormData.csv&quot;, nrow = 1)
names(storm_table)
</code></pre>

<pre><code>##  [1] &quot;STATE__&quot;    &quot;BGN_DATE&quot;   &quot;BGN_TIME&quot;   &quot;TIME_ZONE&quot;  &quot;COUNTY&quot;
##  [6] &quot;COUNTYNAME&quot; &quot;STATE&quot;      &quot;EVTYPE&quot;     &quot;BGN_RANGE&quot;  &quot;BGN_AZI&quot;
## [11] &quot;BGN_LOCATI&quot; &quot;END_DATE&quot;   &quot;END_TIME&quot;   &quot;COUNTY_END&quot; &quot;COUNTYENDN&quot;
## [16] &quot;END_RANGE&quot;  &quot;END_AZI&quot;    &quot;END_LOCATI&quot; &quot;LENGTH&quot;     &quot;WIDTH&quot;
## [21] &quot;F&quot;          &quot;MAG&quot;        &quot;FATALITIES&quot; &quot;INJURIES&quot;   &quot;PROPDMG&quot;
## [26] &quot;PROPDMGEXP&quot; &quot;CROPDMG&quot;    &quot;CROPDMGEXP&quot; &quot;WFO&quot;        &quot;STATEOFFIC&quot;
## [31] &quot;ZONENAMES&quot;  &quot;LATITUDE&quot;   &quot;LONGITUDE&quot;  &quot;LATITUDE_E&quot; &quot;LONGITUDE_&quot;
## [36] &quot;REMARKS&quot;    &quot;REFNUM&quot;
</code></pre>

<p>A more detailed description of the columns can be found <a href="https://d396qusza40orc.cloudfront.net/repdata%2Fpeer2_doc%2Fpd01016005curr.pdf">here</a></p>

<p>For the discussion of damages to person and property only the following columns are   relevant:   </p>

<pre><code class="r">
storm_table &lt;- read.table(&quot;./StormData.csv&quot;, header = TRUE, sep = &quot;,&quot;, colClasses = c(rep(&quot;NULL&quot;,
    7), &quot;factor&quot;, rep(&quot;NULL&quot;, 14), rep(&quot;numeric&quot;, 2), &quot;numeric&quot;, &quot;factor&quot;, &quot;numeric&quot;,
    &quot;factor&quot;, rep(&quot;NULL&quot;, 9)))

names(storm_table)
</code></pre>

<pre><code>## [1] &quot;EVTYPE&quot;     &quot;FATALITIES&quot; &quot;INJURIES&quot;   &quot;PROPDMG&quot;    &quot;PROPDMGEXP&quot;
## [6] &quot;CROPDMG&quot;    &quot;CROPDMGEXP&quot;
</code></pre>

<h2>RESULTS</h2>

<h3>What category of weather has the biggest impact on human lifes?</h3>

<p>In order to answer this question we aggregate the fatalities and injures by weather type:</p>

<pre><code class="r"># maximum number of rows shown in this publication
max_row &lt;- 40
library(plyr)
ev_table &lt;- ddply(storm_table, &quot;EVTYPE&quot;, function(x) colSums(x[c(&quot;FATALITIES&quot;,
    &quot;INJURIES&quot;)]))
num_rows &lt;- if (nrow(ev_table) &gt; max_row) max_row else nrow(ev_table)

</code></pre>

<p>The following table displays the 40 most dangerous weather patterns in the US:</p>

<pre><code class="r">s_data &lt;- ev_table[with(ev_table, order(-ev_table[2], -ev_table[, 3])), ]
s_data[1:num_rows, ]
</code></pre>

<pre><code>##                         EVTYPE FATALITIES INJURIES
## 834                    TORNADO       5633    91346
## 130             EXCESSIVE HEAT       1903     6525
## 153                FLASH FLOOD        978     1777
## 275                       HEAT        937     2100
## 464                  LIGHTNING        816     5230
## 856                  TSTM WIND        504     6957
## 170                      FLOOD        470     6789
## 585                RIP CURRENT        368      232
## 359                  HIGH WIND        248     1137
## 19                   AVALANCHE        224      170
## 972               WINTER STORM        206     1321
## 586               RIP CURRENTS        204      297
## 278                  HEAT WAVE        172      309
## 140               EXTREME COLD        160      231
## 760          THUNDERSTORM WIND        133     1488
## 310                 HEAVY SNOW        127     1021
## 141    EXTREME COLD/WIND CHILL        125       24
## 676                STRONG WIND        103      280
## 30                    BLIZZARD        101      805
## 350                  HIGH SURF        101      152
## 290                 HEAVY RAIN         98      251
## 142               EXTREME HEAT         96      155
## 79             COLD/WIND CHILL         95       12
## 427                  ICE STORM         89     1975
## 957                   WILDFIRE         75      911
## 411          HURRICANE/TYPHOON         64     1275
## 786         THUNDERSTORM WINDS         64      908
## 188                        FOG         62      734
## 402                  HURRICANE         61       46
## 848             TROPICAL STORM         58      340
## 342       HEAVY SURF/HIGH SURF         42       48
## 442                  LANDSLIDE         38       52
## 376                 HIGH WINDS         35      302
## 66                        COLD         35       48
## 978             WINTER WEATHER         33      398
## 877                    TSUNAMI         33      129
## 888  UNSEASONABLY WARM AND DRY         29        0
## 919       URBAN/SML STREAM FLD         28       79
## 980         WINTER WEATHER/MIX         28       72
## 842 TORNADOES, TSTM WIND, HAIL         25        0
</code></pre>

<p>To summarize the most deadly weather patterns in the US the 5 highest ranked are display   in a pie chart. All other weather related causes are grouped in &ldquo;OTHER&rdquo;.</p>

<pre><code class="r">
slices &lt;- c(s_data[1:5, 2], sum(s_data[6:nrow(s_data), 2]))
lbls &lt;- c(as.character(s_data$EVTYPE[1]), as.character(s_data$EVTYPE[2]), as.character(s_data$EVTYPE[3]),
    as.character(s_data$EVTYPE[4]), as.character(s_data$EVTYPE[5]), &quot;OTHER&quot;)
pie(round(slices/sum(slices) * 100), labels = lbls, col = rainbow(length(lbls)),
    main = &quot;Fatalities by Weather Pattern&quot;)
</code></pre>

<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAGwCAYAAABFI3d+AAAEJGlDQ1BJQ0MgUHJvZmlsZQAAOBGFVd9v21QUPolvUqQWPyBYR4eKxa9VU1u5GxqtxgZJk6XtShal6dgqJOQ6N4mpGwfb6baqT3uBNwb8AUDZAw9IPCENBmJ72fbAtElThyqqSUh76MQPISbtBVXhu3ZiJ1PEXPX6yznfOec7517bRD1fabWaGVWIlquunc8klZOnFpSeTYrSs9RLA9Sr6U4tkcvNEi7BFffO6+EdigjL7ZHu/k72I796i9zRiSJPwG4VHX0Z+AxRzNRrtksUvwf7+Gm3BtzzHPDTNgQCqwKXfZwSeNHHJz1OIT8JjtAq6xWtCLwGPLzYZi+3YV8DGMiT4VVuG7oiZpGzrZJhcs/hL49xtzH/Dy6bdfTsXYNY+5yluWO4D4neK/ZUvok/17X0HPBLsF+vuUlhfwX4j/rSfAJ4H1H0qZJ9dN7nR19frRTeBt4Fe9FwpwtN+2p1MXscGLHR9SXrmMgjONd1ZxKzpBeA71b4tNhj6JGoyFNp4GHgwUp9qplfmnFW5oTdy7NamcwCI49kv6fN5IAHgD+0rbyoBc3SOjczohbyS1drbq6pQdqumllRC/0ymTtej8gpbbuVwpQfyw66dqEZyxZKxtHpJn+tZnpnEdrYBbueF9qQn93S7HQGGHnYP7w6L+YGHNtd1FJitqPAR+hERCNOFi1i1alKO6RQnjKUxL1GNjwlMsiEhcPLYTEiT9ISbN15OY/jx4SMshe9LaJRpTvHr3C/ybFYP1PZAfwfYrPsMBtnE6SwN9ib7AhLwTrBDgUKcm06FSrTfSj187xPdVQWOk5Q8vxAfSiIUc7Z7xr6zY/+hpqwSyv0I0/QMTRb7RMgBxNodTfSPqdraz/sDjzKBrv4zu2+a2t0/HHzjd2Lbcc2sG7GtsL42K+xLfxtUgI7YHqKlqHK8HbCCXgjHT1cAdMlDetv4FnQ2lLasaOl6vmB0CMmwT/IPszSueHQqv6i/qluqF+oF9TfO2qEGTumJH0qfSv9KH0nfS/9TIp0Wboi/SRdlb6RLgU5u++9nyXYe69fYRPdil1o1WufNSdTTsp75BfllPy8/LI8G7AUuV8ek6fkvfDsCfbNDP0dvRh0CrNqTbV7LfEEGDQPJQadBtfGVMWEq3QWWdufk6ZSNsjG2PQjp3ZcnOWWing6noonSInvi0/Ex+IzAreevPhe+CawpgP1/pMTMDo64G0sTCXIM+KdOnFWRfQKdJvQzV1+Bt8OokmrdtY2yhVX2a+qrykJfMq4Ml3VR4cVzTQVz+UoNne4vcKLoyS+gyKO6EHe+75Fdt0Mbe5bRIf/wjvrVmhbqBN97RD1vxrahvBOfOYzoosH9bq94uejSOQGkVM6sN/7HelL4t10t9F4gPdVzydEOx83Gv+uNxo7XyL/FtFl8z9ZAHF4bBsrEwAAQABJREFUeAHsnQd8FEX7x58ryYXQe28CojRpojRBEKSJvReUZu/9b+/l9VWxd1AUxa7YfW2IXSwgoNJbCDUQCLnLlf3/nuQWjzPlkrvL7d39Hj4/dnd2dnbmO5t7dmZndkVoJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACVidgs3oGmb+0J3AACLQtg8JihC8rY19pwXYEBkrbUUrYOIRp/HeD+8K3zUPMNJsi4CBoA/STuTOGyzZIqye0CloARWsNkcBAqBD6NCQxDdN926G5IeEjsF4jGKb7YmEmO02rLL6xOE9oGspQWZrmxcpO6DdolxlYiWVoGczDSgsz93FJAiRAAiQQJPAUlkYZuipCSnUR737o/AjjazR1fHpe/bFWC9/ugbAvoJa6E6YOSuO/oRtxsIlIU9N/PEZpNw2mpw4uO5im3vBvCYbnY+kIhtfE0gf5IWUZrZVWHx4kquWLtz2HE+h5wrUGYYdW4uTh9a+HlhZWiSQZlQRiS8AZ2+SYGgnEjcD7SPnTsNTnhW2XtXkddlwaVFlxKgq/BxEyINMJvYP1dpBpS7FyJ/SHGWDx5UbkT/PcCeoLzYV6Qdp6V6sN9YF+hHS/OvtfoB1QtBaL+og2D3OQwBeQ3rwcCWkZn4X2gSKx8PrXY0oLiyQtxiGBuBCgg48LViYaBwLfIM1p5aR7IPYdAukPtHbbvwepA9PwgyC14dA26AVIW61DocGQOu4l0CyoECrNtBvX/Hs5HetmS1Z7Bd6GtkLa6g09Xru0J0FdoJXQS1AOZJrm7WioHqRdxF9Bf0EVmZbjCEjPqU5J06wPTYW0JfwgZJqmvy+kDk0faYTa19hQB38wNBc6DFJbASnHoZA6eN2vpnFM0zRPhhpB86GZkB8yrbL1YR6nLKZA2o3+OfQWFGpjsaH53A19CM2D1DKhS6EcSPN/PPRRUFj8y/S4B4KhymsT1B7aH9Jrobzro7T674xjwq8JZVfeNdAd+8dAeqOhN1Na5qehrlAXaAY0FBoErYWegPQao5EACZBAShAwu+jvRWnahqhZSOmOxLoBaTeyOmJd3w51gC4LbmuYSn901V6EdFvj63G6rg7FNHXUGmYPBoRufx/cp/tVV0LhXfS1EbYouF+dkcbT7m/txlXTpYYVQOpQNA9uqBtUmk1EoMZXBxaAzPxsxno7yAYthzROL0hN864tdU27ORRuExCg8d8M7vgkuH1qcPlhMPzt4LbeLKgNgzTfeqxZNr05cUBqVakPvTHR9P6GdgTXdfs8yLT/YkXDvEEpB3XqarUg3ad8zGvgdqyH23MI0HhXhexohXVNU8P1mlEr7/oorf5LC6voGjDr9C+cT8+t0utCbwR1XW9C9JooCm7rOWgkQAIkkDIETAdv/gCay+9CSng11tU5tYHUqanD0nhnQfrD/3hw+2YsG0P1oRmQpq1OSbd3QXqMxlczHaimpxa6rWmshTS+du3qMeOC229gqXYbpPtfgLKgk4Lbc7BU05aa7ldnqDYUmgYdCpVmExGo8fXH/mBIW4tvQRr2DKR2A6Tbd+kGbCik2x9Bpdk+CNT96hQ1j+qs10F2aAukjtIJ5ULqTLW1rvYnpMcdB+lxs4LbZ2CpVtn60GNMB6/H6s3KmZCe43NI7QBI87AGag2pU94Aab1pvrQONL5KW+SHQBon3J5DgMb5BVInrgz1JknDFkJqFV0fpdV/aWEVXQMTcS49r95cjIf0GlILdfBarjaQeSPVRCPQSCASAvrHSyOBZCAwH5lcEJJRba2adg9W9If7cGggpA5QrSakDkAdlZq26vXHXO1MqBt0ETQIyoDUsiE9pjzTNHzBCKaTCY+vDkZN454IqdNUJ2Y68BVYV9PW8d/Qp9Dr0JdQefYNdn4fjPA8lkdBfUO2b8b6CdC10HGQ2gsli3/9r3lQ594COhnS7uT/QQHoc+h4SNNoCi2GtkDqYDpDbkidj5ZtE6SmZdNzVaU+9Hg1PV6dntnTos5WbTCkjn8VNAxS0/wPgPpBcyHTbsRKRV3ZvRBHpablVf5TdQOWB50JlXV9aHnD61+vmfCwQxCmVtY1ULJX5DOsvGtuhCxnY13TVa2G9oOUh8kbqzQSKJsAHXzZbLjHWgS0ZXxXGVm6BuF3QPpD+DGkLczmkP5wl2bqmPQHVR2S3ih8BO2AtBWmziUWpudQUwc8unitxCHpak1InaCeaxK0b1DnY6kt2HuhskydhWn6o69mnmsN1tVZjIDU6R0D6c2N3kSUZV9jhzppvSFQUwevpkt18MpWzXSg5rnsCLuqeE/Jf0uw0JaoWmXro+Sokv+1t0DNdJbmb5R53v2xTxmZpufVXgTTCrBSkXPXuHdCj0N6zWjdh9a7nisW14eZ57KuAZym2PQmsTQLrWvtuVHTmxwaCUREQP9IaSSQzAT0R/RWSB1Za+gESH/0Q8109Kaz0B9cde6vQR2hCyHzB95cIqhcC08zPLLpKG/Hji5Qb+hKaAykTkhbj2uDy7ZYXg6pnVuyKPP/g7DHLIfZQlwREnt6cP1+LPUm53VodzCstIU6eLVOJYviGwRdNfOvXeNqpoPXc62C1NEMgrRsyvNsSB1+VeoDh+2xsvib+fkJMfWcKmV1MjQHMs10hOZ2WUt16uug7VD4OSO5Pkqr//AwM89lXQNm3jzmStjSF7bNTRKoFAE6+ErhYmQLEtAfx61QHUh/7C+AzoTUNExNnb/asdDVkHZLq6nzOhp6BmoCqZnHlGyV/b+Z5o2IMq6UaB8Ew27B8lJIHe97kNkiPg3rL0IzoQOh5pDa6pJFmf/rTcwC6F7oZkjt2ZJF8f/6TFmdlz6qUNP0yzPTwWucxZDZmlRHrjLNdPC6/T6UAekNknZrvwN9BWk5qlIfOKxC00c0GyHtnXgQuhxSB6rlzYZMM52suV2VZSTXR2n1Hx5W0TVg5i38BsMM55IESIAEUprAUyid/gBeW04pz8A+dYwabw10W3Bdu/XVukJ6E6D7Tac1HevaPathH0MaV9dPgtQKId02b4LDty/GPnUmGuceSJ28rpvnxKqcA22DNFyd7myoBqSm3crPQWsh3a/6EmoPlWYTEahx9FzqTHVdW6vaOrRBofY4NnS/MgnfFxpP17V8eZDGnwaF2pPY0PBloYFY11b6LEidue5XppdApp2BFT237ou0Psy0zDT2DR6/0AzAsgek25quD/oW6gepaZ40fItulGPKXONdVU4c3TUdKu/6CK9/Paa0sPKuAbNOtb5C7SVsaB6PCwn8PRi2X0gYV0mABEggLQioI2tRTkkzsa8NpA7NNG35NTA3qrCsj2MaR3BcS8RxlhNPW+W1y9lf2q4mCNSbhNLseQSqg7ijtJ0xDNNWfFnMq1IfkWatHiKq4m0VXR+l1X9pYZrPiq6BeJeF6ZMACZAACSQxgQuRd+0WDkDautcbGhoJkAAJkAAJkECSE3gC+Vfnrt3jpyR5WZh9EiABEiABEiCBEAKhjyBCgrlKAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiSQaAJOZKBBojNRifM3QlxbJeIzKgmQAAmQAAmkDIHTUZL8oDxYFoVsT8W6WjtoDlQA/Q0tgm6CTOdZB+sGtB5aG9Q6LN+CmkJq50M7oTa6EWKfYf3kkG1dnQbpuerrRohp3EJI86tprYHugmpAprmwciu0DVoFrYZmQ80gGgmQAAmQAAmkJYH7UepHwkpeE9vqJK+HTCeprfgvoBmQmungtcVsmjrnd6FngwHq4NU5fxDcNhfhDl57CVZBT0IXQKGmcacEA/TmQm8W/gfNDIbp4nHoQ6i3bsAyoKuhpVALiEYCxQTs5EACJEACaU7gHJR/GXQ7lBtkoa3jo6HToebBsPBFHgK+geqF7FDH2xQ6NSQsfHU0An6F1MFPDt8Zsq09BtqCPxc6DdJ8tIPOCuoXLNW80D2QluE4iEYCxQT0TpJGAiRAAulMoBsK/2MpALYjTJ1mH2hucP8QLHdA+tu5L6Qt50mQaX6s6A3De9DH0BYo3M5AwKuQOuhsqC/0M1SWaR60u17Pp70N6yHzRgSre2w+1jQtGgkUE6CD54VAAiSQ7gQaA8AfZUDIQXjLkH23Bdc7Yvk7NAz6LRhmLn7CyuvQg5C2vENNW/vDIW2Fq+mz88lQeQ5eW/Lq0BtCtaHNUGmmeT24tB0MS08CdPDpWe8sNQno8926kD5r1mfJ6ni0dVjTIVILO2rjwW4W1rPwI5ERwGAzeBkfFEAT1bNLZBdGiGlLthDSwWL5kG5vh7ZCiJI0thA5bV1GbvUZ+G8h+w7BurbKe0BfQHpc6H5sFtt1+P9PSLvjQ+0EbChn7aJXK2aO5eWQcizL9DxrIeAvM68aR286aCRQTIAOnhcCCaQuAR18pa3PtlAbeKouNUQ6wrO3hJdo3EBsdnh3exOxZeDhbmZtsWXBsbtqii1TvY4O0NEEMjCQHA4eMoqHkcPJF3sitxjGbjz/hUcv3CKGG17PmyeGdz1iwesX4eZgM+KuxsPsvzeJ/IXD1EGthrSlqclYxb5DRh6GtLvdE5KpflhvBanTzAwJ19UF0PXQDKgrFN5lrjc6l0FPQBsh087AyjXQJ2YAltqKPx6aAZVmIxCoI/8XB5d6YzYY+hoyTX/LNQ3NE40EignQwfNCIIHUIKBTp/aDOsOb98sS6YX1tvuIzbGv2LKh+u3FntEYgXDoxaPAHHtmgCGwaqa9AOr4VOp09hicv/Yjd94oxqAcrK/FDcDfYuT9gWWuGEU4cMVOkZ/g6fW5sbZ0l0F6H5EIm4OTToBmQlMhdc7doRegSyA3pGUMN3XeE6F7ID0+3GYhYBKk3fhqHaA+0GgIxd9jL2JtMjQjGKL3V7j3Kp4apz0FDwdltvCnYHsGpA5dn+PXgR6BlkP6aIBGAsUE9A+URgIkkHwE9Ee9Z0ORAXDaQ/CH3Km72DMPFlujzmKvAccu7RDBGb0TjzmZQjj8VdAK6Hcxdvwoge3L4fjRpF+4FlPC0Cug3dfaQlbHGmu7Hwmqs74gLGF1qo9CI6EMKA96BroXUlPe+ggCuPcaOHcQtr+FBkG9oaGQOl7T9sXKQuhMqDPUEzoKCjXck8lKSHsC1FGbNwQ6On4dNBO6BQq9AboQ21OhZpDa55A6/nzdoJGAEqCD53VAAslBwI5s9oCXGYSJzuMyMaJ6IBz5ILE16SF2e0f8KVvRmUeKtgjOfiG0QALer8XYNF8Cu31w9GiSvofHCd8jnaWRphWM1xTL4yB12pUx/U3U1rOOI0gG07xqj0Oo80+GfDOP1UCADr4aIPMUJFBFAjVw3EGtRY5E83JEV7HXGSW2Zgejqx1d7nhGnrp/vurwf4N+kEDhpxLYhBb+JjSp391cMvXsF3CB3y/XrsTe9dCscmNxJwmkMIHU/YVI4Upj0VKaABrn0r+9yAl4jn7YIWKvO0bsTQfBnddJYYdeUY3qwL154g98KMaGnyWwDc7+LQzcewfHaXc+evf3slrYegU6FdJudRoJpCUBOvi0rHYW2oIEuqFP+cT6eHsanHmjY8XRdAAcenYaO/Wy6ggj9jE/LRB4QwI5CyWwMVfkeTx4fhvx8Qi/2E7E/7hHkrtLNvk/CaQnATr49Kx3ltoaBOo4REbBE53dQWydJoqjxVCxOzB9zRq5S4JcaMv+PfG7X4Sjh+P/CUPxn0K2J0E3QTo1j0YCaUuAvyRpW/UseAIJtG8icgaGY598nDiaH4ln6xgBn8DsJP+pdY7+j9AT4tv6hRjbC0T+i755nTKGx/Y0EkhPAnTw6VnvLHViCPRBa/3CFmIbOknsLceLw1mXrfWY14S26t8Wf+F08a/Hc/rXN4g8jZOsiPmJmCAJWJwAHbzFK4jZSwkCh3TAq0j3E1vvi8TZagicegxeMpMSYOJZiAI4+jni9z0ugXV42c4nq0reDb8knudk2iRgJQJ08FaqDeYl1QgM3Efkun5i73kuuuIHsBs+IfXrg6P/GIPyHhD/Orxg53+r0X2PjOhrX2kkkNIE6OBTunpZuAQR6I0W+03dxXbgZeJs3p+OPUHVsPdp/SWO3rhf/GuXifEeJsnrW+rg72kkkJoE6OBTs15ZqsQQaN9G5P86i23U/6ErXueu06xHQB39WxLw/Vf8a9aIMRPP6R9CLrdZL6fMEQlER4AOPjp+PJoElEAtjIi/CO9/n3S5ONqOx1Q3PmO3/oWxG47+JUyxe0T8KzAC7y68+F5fjoM35NJIIDUI0MGnRj2yFIkhoH8/Y/EFkdsmiaPT2eKoyRfTJKYiojmrfu3ufvHlvSeB35eL6Ctuf44mPR5LAlYhQAdvlZpgPpKNQLt2eIY7ROxD0B3fRL/eRktuAr/iey3Xim/dIjFewdvx7kBp9CMuNBJIWgKOpM05M04CiSGAuesypYvIE3eLc8AV4qxdn849MTUR47M2Rz2egJcO1cNnX/Ep2/FbSgbg4eV4NBJITgJ08MlZb8x1Ygh0bIdvc58u9jMfl4wWB+AzrYnJBs8aLwI6dqKP2J1j8S2AtRI4bKdIJ+g7nK8wXudkuiQQLwJ08PEiy3RTiYAdn2ud0B3vOb9fnAdOFWeNGmy1p1L9/qss+obBY8Req6FId0ypG4P33f6JSKv+FZEBJGBhAnTwFq4cZs0SBBrje+xPHyv2c5+WjJbdxM6H7Zaolvhnwg4n3xMzIg4XR7OlEhiJL9Y12F3SmudI+/jj5xliQIAOPgYQmUTKEhiIEfKv3CSOIVdLRi2OkE/Zei63YDrGAjd4tQN4No9584fgW/Tf4AAsaCRgbQJ08NauH+YuMQTsGGh18UCx/fdZydh3mGBaOy2tCTjh5AeLPbOn2NpjtP1oDMBbaYgsTWsoLLzlCfCHy/JVxAxWM4F6rUSeRIttCrrkm7bg2+iqGb+1T9cGj2jGiKPhcgkcihfjFOGTtL8ix2jc00jAegTo4K1XJ8xR4gh07Cjy2jXiGIG57bUy0WqjkUA4AR2AdxS67PFu2wFrxTgATv5zxOEo+3BQ3E44ATr4hFcBM2ARAoMxSv7lpySjl36n3UbnbpFqsWY2tMt+mNiz6mMa3WIxhsHZz0VO+T57a1ZX2uaKDj5tq54FNwnUxhS4g8X2yAuS2QFz281gLkmgQgK4Xhy9xd7qF8yZ3yjyCw5YV+FBjEAC1USADr6aQPM01iSAec7XjhD7dTMwBa4lW+3WrCSL56p1SWu+8XwxhuPLdGv8JXPmLZ5rZi8dCNDBp0Mts4ylEcjsAOc+RuyXPy7OxrXp3EtjxLAICTTE9YNrqd4SCQyGky/08IM1EZJjtHgSoIOPJ12mbVUCNfDymidPFcckvE++Xgadu1XrKanyVQvX0TgMvlskgQPRXZ+BUXfzkqoAzGzKEaCDT7kqZYEqIOBsI/LCVHEcdT0+FKNvK6ORQKwI6MyLsWKviY/V9MwTw4cR9j8gbUyZp5FA9ROgg69+5jxj4gg06lBPZp7gto+7STKyE5cNnjmVCWiP0CixZ68W40BMo2u4S+QzlJdOPpUr3aJlo4O3aMUwWzEnUKtNG5kx8fyM0W8sC9j65ducLdh6jzlkJlhCQL9Kh8GbNeDku6wrcfKfkg0JVDcBOvjqJs7zJYJAzbZtZeYVV2QcfuWVmVn77GeznTvX6+0PJ6/fAKeRQDwI6OOfw+DkfxWj2xYxaheUvBAnHqdimiRQKgE6+FKxMDCFCGTCuT93552ZYyZPzizulu/UyW5v18lmu2Ce1zsQTr4pnXwKVbe1iqIteQy8c70hgd74BJ0NX6P72lo5ZG5SmQAdfCrXLsvmwOT2hydPdh53wQWZtUJx7Luv3d6yvdjOn+fzDt5pczahkw/Fw/UYEtC33p2KD9UsQkse3fVFeIe9DryjkUDcCdDBxx0xT5AoAk2byp0nneSccNttmXXtpXzGfb/9HPambcR24bc+76Fw8o3o5BNVVSl/Xh14NxKj6/Elup5rRFajNb845QvNAiacAB18wquAGYgHgfr15Zyjj3Zc9sADrkZOvFm+LOvSxWFvjM/HXQAnfxicvL6whEYC8SCgU+gOxTz5byRwMJz8fJwDCxoJxI8AHXz82DLlBBFwuWTMgAH2/zz9dFbz7OyKHXbXrg57/eYiF37n847cZXM2oJNPUM2l/mlr4toaIva6X4t/QG7JoLvNqV9qljBRBOjgE0We540XgQP69LHNePnlrLaNG0f+4Zju3R32Ok3g5L/3eUfBydenk49X/aR9unpt9RR7o7kSOAifn3sbQDD2jkYCsSdABx97pkwxcQSa7Lef7fXp011d9t238pf2AQc47NkNxLj4B593LJy8fvebRgLxIKAfNmos0vhnzJPH2+7ewjnwjRoaCcSWQOV/BWN7fqZGArEikNmunbyI6XADhg93Vvm67tXL4cisByf/o883rsDmrEMnH6v6YTphBLrhU7NourdYL8b2rSI/hu3mJglETaDKP4RRn5kJkEAMCTRrJnfeckvGMaedllkz2mR793Y4bDXFuOJnn/cIOHl+aS5aojy+LAIHi831rQT6LMPIejThObK+LFAMrxIBOvgqYeNBViKAQXVHDB/uuP7WWzMbwTXHJGsHHuhw+FyGcdUvPu94OHn9UhiNBGJNQF+EM0jstb7Ai3A2iPwP6XPQXawhp3F6dPBpXPkpUvR9unWzvTBzpqtt/fqRD6qLpOwHHeRwuJ1G4Jpffd4j4eR1BDSNBGJNQG8ee4qtwVcS6IVBd68i/aJYn4PppScBOvj0rPdUKXXWPvvYZj3xRGbfHj1i1HQPI3PwwQ7HThuc/G8+33G7bc4adPJhhLgZCwLBDx81XCBGfXx97qNYpMk0SIAOntdA0hLAm+qumTLFeeKECZmueBZiwACHY7vfCFz3u897DJ18PFGnddq9xZbxowTa/S2yFN+W/SutYbDwMSFABx8TjEwkAQQOPvhg+z0PP+xqnJER/67zQYMcji0+w3/DgpKWfBZb8gmo8tQ+pX59biCex38q/gM2ibyB0qIxTyOBqhOgg686Ox6ZOAJ1One2vTxjRtb+LVrE9rl7eUUaPNjhzHUbgZsW+rzHF9qcLjr58nBxXxUI6IwNfBOh/g8SaJMv8mYVkuAhJLCHAB38HhRcSRYCmBJ3y+WXZxwxbpwzo7rzPGSIw7F2l+G/9Q+f7wQ4eX2/OI0EYkmgi9jt+PJcs7/EWIuP0vwRy7SZVnoR4K9TetV3KpT2oJEj7a+/+25WK5crMZevYRhy/fVFni+e8BsfbHNm1ZLq60VIhQpkGSomsFEMGSNFfy4QfJ9GBK+tp5FA5QmwBV95ZjwicQRqoGt+Fj4i07Vly8Q5VZsNXwU71OFctt3w37XI7z/RbUNXQmJuNhJXFTxzPAno1Ll6IvV+EqMRuurfjee5mHbqEkjcr2TqMmXJ4kSgQQM559RTHd169Ej8Zavfl8drcV0DJjlkTF2fxy2BOJWayaYrgePF4TxQbGNQ/sPSlQHLHR0BNjui48ejq49A+759bZ/+7381OtSta53L1u835Morijw/P+eXj/Kdrkx211ffFZEGZ1qGrvqjpOgnzJkbiuLyq3NpUOexLCK76GNJk2nFjUDbtvLIvfe6BuGFNolvvoeUUlvyh41wOH/PNfwP/On3n+Sx4Us31rkBCckqV5OQQANcS/jaXJ1fxdjpFvk+CYvALCeQgKV+LBPIgae2NoEhffs6ho0d67DkDam+//7++zNdXU9zGEfU9nl87K639tWUZLk7Txy124ucj2y3TrKsM7sJJmDJH8wEM+HprUXA2amT7fmHH87s2ry5de9HtSU/8nCH84e1hv/Rv9CSL7I59cUlNBKIloC+VKmh2OpibnxNDLh7P9r0eHz6ELDuL2b61AFLWg6BmjXl+COOcHTt2dP696JOp01wI+Jqd6JDjqzl8wTYki+nZrmrMgTG49vxHcU2Fsf0qMxxjJveBKz/q5ne9ZPupc/u0ME249FHXe2sNLCuvErR7vpRoxzOuSsCvqeX+gOneK35WKG8MnCf9Qhob1BLtOLnSaDp9pIvzlkvk8yR5QiwBW+5KmGGTAJ168ppp5ziaN+6dXJdppmZNnn8CVdWs2PtxlHZRfz0p1mhXEZFYBhmaPQV+8FIZGBUCfHgtCGQXL+c1Vct+vC0ThSn01eoNijn+Ir2l3No2uyq26qV7eKpUzNrJ2OJ9S17jz/pctU/2h44NrvIk4xlYJ6tR+AicbTYR+T/rJcz5siKBFLJwU8EYLy6WTAOZS+ZU0vuQvjvUDZk2nisrIWaBAOaYvk6tBr6CfoOOh0y7UOsFELh57g6GKEZljoIZjP0K6SvmJwKmVbR/vsQ8TGoJaRlaQeF29cImARVVN7Q42pgA1+gFD1/qF2MjXeDAZGmNwjx/dAZweN0UQsymRRgXd/6Ym4rs0pbnTpy6llnOds2bZq8A9Wysmzy1NOurJrj7cYJNdiSr/RFwAP+RaA/WvG9xd4bOwb/aycDSCCMQCo5eC3aPEhb3qHSLi21GyHtLn1aN2AHQC9Ap0H4OmOx8/sGS02jA9QZUud+P3QIZNoVWAlNX9fvCe68G8vlUCOoLaTHPQj1hdQq2l8SS2Q9Vj6DTjIDgst2WPaCXgtul1feYJRKLSJJ7wyk+BA0OSTlXVg3mQzH+paQ7dEh8SJdrdmype2800/PqBnpAVaNV6OGTZ59zpWVMc5unEQnb9VqSqp8XSqOZviBMhsVSZV3ZrZ6CaSagy+Pnhc7T4HGQ+q4X4X+A30FqekrIVdB6pA1rtoy6AhIbwAiMXVI2lLW1rfa39BgaJ1uwCraXxKr5H+9+Qh38Kci7G1IW8eJsCycVHnozVJzaF8o5oaR8yecdJKjdZMmydt6D4WiTv656S6XfYw9cFoWW/KhbLheeQL90IrvIja90T+w8kfziHQi4EyxwrZEeS4MK9OP2P4hGLYUy4ug6ZC2kLXb3jRtZf9iboQsvw9Z19UhUPiN0UsI2wbpDcMnkLZi34W0u/5bSJ2+WkX7S2KV/P8WFo9D+0NLSoKKextCy1dReYOH7VncgjXtQjdNu/pCbxYqSk+duz622AnNhrQVfxUUS8to3dp24aRJGXVimWii06pZE05+hitrwhke94QPirzPezJ1HAaNBKpE4HxxtvhDvBevLOmBrFIaPCj1CYQ7qmQvcX0UQJ1rqNqHFWo5th1QHhQI2dcU66HOLmTXXqvadR+avq7rM2g1vZlQh6w3EIdCc6EPoNqQWkX7S2KV/L8bi9cgsxXfB+vaA/A5ZFok5TXj6lLrO1ThTeSK0tPu+Vc1IZg6+AlQrB3VyMMPd7RJ5NfitHDxsFq1bPL8C66sgpH2wCRXkTce52Ca6UFgGKbNtRWbjofplB4lZimrQiDVWvB/AMJR5YBQB6nO93roGuhI6B1IbRFU2h9LE4T7oa2Q2lPQo8Vr//6vLoI2QP8Nqh2W2qWuvQZ3QBXtR5S97AVsPQ3dBJ0GzYRCb0oqKi+i72U3YCs3JORirOsNimnlpaccRkHdoduCB2jYEdCbwe2oF3hr3SVnnulsGHVCFk1AnfwLL7pcp53i8Zz9aZH3ySK25C1aVZbOlg0OforYW/0t/kk5Jb9lls4vM5cYAtqaSyfTLvIVkDrb/4Meg+pBar9Ag6Fs3QixR7Ae2pUfsmuvVRe28LcmoT0Gq7D9ErQfVNF+RPmXzUVIBqRd6dqSfx5KlJ2ME/8PGgvpTZTqfki76atiN+OgqVCLkIP7dO9u3y8Z3loXkudKr9apY5OZL7lcG4fb/edlFPkqnQAPIAEQGCMOB7rcjsGq+RtGLiSwF4FUc/DqDBuUIi30YZC2gk2HpK3w1ZC2ttXehVZC2kLXlmkWdCY0GroZMk1vAMLPoV30Otf5I+hZaB9IrROk3dqfQxXtR5R/mYGQF6BbIM3b31ColVfe0HiRrpeXnpbjRUh7OkzpDdJIqDVUWXsYB9ggrQetgyGtW8tZEyc6dRxAypu+mQ9OPmvdoTbfRXTyKV/f8ShgLfz5nCKO5vihOjoe6TPN5CeQag5+AKpkaynSLt/nIO2WV6euFoAmQadCI4LbegNQG9JBbRuhM4PSlrlp92Il/BwvBXdOwHIbtBDaDf0M6SOAGZBaRftLYu39/0xsjoNm7B1cvFVWeTNLiRtJUFnpHYCDu0B6ExRqy7GhZTwrNDDCdWX4JHQ89B50Al7qevSwYXjXa5pY/fo2efHlrKxlh9j8lzrZkk+Tao9pMY8XRy3cXU+JaaJMLGUIpM2PaSVrTG986kDbK3mcGV2P10F7uZC2wsOtov3h8VN+OytLJtx0U8Yj11yTqb0haWVbtxpy0vFuT/evxXGvL8OZVoVnYaMmcJJ4178hAX1kpjfbNBLYQyDVWvB7Chblirbuq+rc9dR6vA62K825R7Jf46SVtW1rm3zccc60c+5ayQ0b2mTW7CzXbwPFf43D60+rimdhoyZwqthboBV/StQJMYGUI0AHn3JVmpQF6oqvxrXt2DF9L8fGjW3yyqtZrvkDxHeDnU4+Ka/iBGX6MLHb0N2og191lhCNBPYQSN9f1D0IuJJoAk2bytGnnOJsnuh8JPr8+ua+l191ub7tL75b6OQTXR1Jc/4aGGw3SuyNkOGhSZNpZrRaCNDBVwtmnqQcAg4MNjvu8MOdfPYMSM2a2eWV11yuLw8S35108uVcNtwVSmCcOBp0ZDd9KBKugwAdPC+DRBPo06ePvVGjRhzvaVZE8+YlTv7jfuK72+bV8Rw0EiiXQH+04jH95yBE0im8NBIoJkAHzwshoQSaNZMx48c70r57PrwS9FW9s9GSf7+P4b2PTj4cD7fDCDjg4PHiG50OPChsFzfTmAAdfBpXvgWKbscLX44aMsTJ67CUymjVCk7+jSzXO70N74PiLWtGRilHMigdCQwXe70OIselY9lZ5tIJ8Ie1dC4MrR4CXXv0sDVo2pTd82XhbtPGLi+/nuV6tZdR9LD46OTLAsVw6YdWPN5q1x8o0nK6KS+BfxOgg/83E4ZUE4E6dWQIBtc1q6bTJe1p2rUracnP6hkoelz46vqkrcg4Z9wFBz9M7PqCrr5xPhWTTxICdPBJUlGpmM0WLWzj+/d3ZKRi2WJdpvbt0ZJ/Lcv1/AEBz9N08rHGmzLpDRF7EwxoGZ4yBWJBoiJABx8VPh4cBYGGaMF37NKFl2CkDPVFQOrkn+4e8DxHJx8ptrSKdyAmRmE0/ai0KjQLWyYB/rqWiYY74kygz9ChDu1OpFWCQKdOdpmFN9490S3geZ5OvhLk0iNqC3TTtxWbvvSmXXqUmKUsjwAdfHl0uC9uBFq2lCEDBxZP64nbOVI14f32s8uLeHf9w10CnpeEr65P1XquarkOEbtOl+tV1eN5XOoQoINPnbpMqpLUrm0b1qsXL7+qVpo+2lAnf//+fs9sOvmqYkzJ4/qKvXZbwXg7WtoT4C9s2l8CCQHQpH59adq6NS+/aOh362aXma9kue7dz+95g04+GpQpdewB6KbP5AtvUqpOq1oY/sJWlRyPi4ZAl379HPzyVTQEg8f26GGX5192ue7o7Pe8TScfA6LJn0RjOHg8i6+LkrRI/tKwBNEQoIOPhh6PrRKBevWk14EHFn/9qkrH86C9CfTs6ZAZs1yumzv5PXPo5PeGk6ZbfcWGwfTSJU2Lz2IHCdDB81KodgLNm8shPXo4eO3FkHzv3iVO/oaOfs8HdPIxJJucSfUWW0N8daZncuaeuY4VAf7Ixook04mUgC0QsO3fsSNfTxspsEjj9e3rkOdecrmu7eD3fEInHym2lIy3v9htjfkcPiXrtjKFooOvDC3GjQWBVi1b2lw1atDBxwJmeBoY2yDPzHS5rtzH7/lM+KXZcD7pst0Bz+FR+53TpbwsZ+kE6OBL58LQ+BHocMAB9uz4Jc+U8fpfefL5zMzL9vF5vqKTT8sLIhsOvpnYXCg8B9ql5RVQUmg6+DSu/EQUPStLOvbsWfwijkScPm3OOWiQ0/b49MzMC9v7PHPp5NOm3kML2k1sNbC9T2gY19OLAB18etV3wkvbooX0xPx3R8IzkgYZOOQQp+2x5zIzL2jr83xPJ58GNb53EbuKDePspPXeodxKJwJ08OlU2xYoq8tl69y6NZ+/V1dVDB3qtD30TGbGVDj5H+jkqwu7Jc7TWmyZ8O7dLJEZZiIhBOjgE4I9fU/q90sbOvjqrf/DDnPaH3wqM2NKG59nPp189cJP4Nla4Tk83ia1fwKzwFMnmAAdfIIrIM1O36BRI3GiFZ9mxU58cUeOdNr/+0Sm88zWPs+vdPKJr5BqyAFa8HqWdtVwKp7CogTo4C1aMSmarWaYIudM0bJZvlijRzsd/3ks0zmhlc+zgE7e8vUVbQbrlzh4fWVtRrRp8fjkJEAHn5z1lqy5btK2rV1H9tISRGDcOKfjzocznKfByf9BJ5+gWqi+0zYVmzbj8c4bWjoSYGsqHWs9cWVu0r598TuyE5cDnlmOOirDgbEQcurFXs/s9U7XfsL7/FS9LPAcHq13ownKl5OqZWS5yiZAB182G+6JMYE6daRZw4b2rBgny+SqQODYYzMcPp/IiZd5Pa/mOF2d6eSrQNH6h7QS0b+3+tbPKXMYDwJ08PGgyjRLJYCvyLWAaBYhcOKJGY5AQIwTLvd63tzgdHWgk7dIzcQuG41KXnZDBx87pEmVEh18UlVXcmc2O1ua16vHEfRWqsWTT85wakv+2Ku9nrfg5NvTyVupeqLOS0ORbLxVqh6eyNDSkAAfvqVhpSeqyA4HPmHZgA4+UfzLOu/pp2c4L78jw35Mc59nLQfelYUpKcMxkt7WiO+jT8q6i0Wm2YKPBUWmEREBdAfXdunnL2iWI3DWWRkZGHjnHX+D1z0n15nVii15y9VRVTJUA1Pl8GUndtFXBV4KHEMHnwKVmCxFMAzJzs5mC96q9TV5ckaG1yty5K0lTr4FnbxVqyrifOmcVHTR61x4WhoSoINPw0pPYJGza+LdmTTrEjj3XG3JGzL+dp/7vY3OrGZ08tatrAhyFnTwtSOIyigpSIAOPgUr1apFQhdwZgbfqWXV6tmTrwsuyMzAwDtj/F1w8pucWU3o5PewSbYVdfDoM+PU1GSruBjll4PsYgSSyVRMAF30DqeTXfQVk0p8jEsuycw85UqnfWwTn2cbB94lvkKqmIPgX1tmFQ/nYUlOgA4+ySswmbJv59WWTNUlV1yRmXnSZU7bmMY+N518UlXdnsw6S95Hj8fwtHQkwC76dKz1BJWZ3fPVAz4/PyC7dokUFKiMYu3eLaLatcuQwkIJStcNcbtljwoKjcBuj+EvKJJAoUcMrBsLfEZmN3ttf8dApsdR4jCqpyA8S9QEMP/dViC7WojsjDotJpB8BOjgk6/OkjbH+kKVdDC3+x8Hq4529+5/nKzpYNXZqnNVZ+t2/+NkNWyXW/y7iwz/bjjYwiIxPHge7vEahhu/1l4w9GLp84vNHyhZBvyGDVMQi4XHIGLLFAMSm8tmSMm6YcNTWJsL4ZgzhaUIHs7asmw2G+ZR2erBa9cQu62GzYEXCdttmTZ7AE6/8FM4+XmNA7YdXtuZgSnGEXIkjqYlEwGPuGWSnOWhg0+mWotdXungY8eSKVVAQJ1QBVFitrusVqzpcMtqxaozRst1r1ZsoVedrGEUwbnC2RY7WR/KAicrGDho8wXgYLFe7GSxLfirwic+1MkaxU4WDrXYucLJwoEaJc612MGqozWdrN2WbXPA4cLBisOWaYcQR5+eBpfF20jLkYlTlLZPwzKiQ1w4NyB5/3EV+VY0l6zR4w3/1vds+wTscpFc4mwmzWJWP0yoeggsk2WYB5+9pXrOxrNYjQAdvNVqJIXzg+5i15w5vj0t19JasRrm8ZTeinX7DbRky2nFGmjFwsGW2YrVVqu2YkuWFbZiBY7UdLJ7ltr6hSN1Yl9GqOPFerEjVmde/IXO5KrIHU/5JX96A/QntLHXPudCR9bBBztz+h/rdu7q7jhFuvvh3PlbkVxVWpxbv+gdp6RJ31kSVlCcs8w/2jgDZvL/ENhUJDVOfa6oMKpWrDpQONMyW7G6nyP1/4FezppvW0C23xMwdr/f3OPs1MdocNuFNbIOPVR8a9bI+j7jPUbeBc428rB/ijyktzW0JCSgDh5/EUVJmHVmOQYE6OBjAJFJREYAL8Ve03CGs4ejbnTdyJGdjbHKIuD5A93wdzi8Rb+3DmSNGm80eWVyVma3bsXRi517v/FuI+8yp1N+kJPkGGmBf7TkJFAgBZjk6MdIEFo6EqCDT8daT1SZbbLbQCcwX5yZmArY9ZZftj9Uyx3Y3spW+6yptobTTnI5mjTZk5li5953nNvYchF+F45E6/0+z1T5hK33PYSSb6VQCtGG93MIffJVXUxyTAcfE4xMJCICNikw8IydVn0EAkUB2XE/RvW/2sRtr7+v1Lnokozs0aMxWn7vl5v5cnJk/YHj3IHNF2DO9BSnQ871nSBHS0v8oyUvAR1FXyRFO5K3BMx5NATo4KOhx2MrRQCD33YEMEodzwQrdRwjV56Ad412w4vfPbe5z3XwsEDDx86pgYFzpSbky83FM/fR7sCmc+Dcp+JlwtukjXzlP5ut91J5JVNgvui/XRxFn0yVFsO80sHHECaTKp+A4ZNNgW3lx+He6AgUzoNjvwfT3Fa2kOwTTgs0/fCMrIx27cpM1Ldpk6zvPcodyJ0E535e8ZcCHHKD/3g5ymglrco8jjuSg8A22ebbLttykyO3zGWsCdDBx5oo0yuTgLFTNgS2l7mbO6IgkP+MX3Y8Vx+vyGljq33OBY5axxzjtNct/yuhvi1bSpz7hgl4ifBFwc8AbUfr/Uvf2fLh3n34UeSNhyaOwBbZjPcZCv/qElcFCT0zHXxC8afXyQObZa0/X2eps48+FjXv245pbncHp7l17G00uAXT3IYNE5uj4leP+7Ztkxxtua8/Gc79Up3FX2x2tN6PlSOMNnDztOQnsEW26rBWOvjkr8oqlYAOvkrYeFCVCPhlq3+doVN2+H3qKgEsOcizKDjN7TdMcxt5hNFkFqa5de8ecYr+7dslp9dIt3/tsXDuV+5x7oKntW3kc9858gFb7xHTtHbENbIKr6kVPoO3djXFLXd08HFDy4RLIbDRu4oOvhQuEQUVvOOXvGmY5pbXylZrwmRbwwdOdjmaNo3oWDNSID9f1qtzX3M0nPu1Ic4dr9GV6/3HyFijrbQ1o3OZ5ATWy3p8sUA2JXkxmP0qEqCDryI4HlYlApt8a0RbFLQICRRPc3sQ09xmY5pbXUxzu+DikmluNfC+3UpaAC/iX9drhNu/aiyc+3V7OXd8Zw6t9/+h9f4+W++V5GrV6F7xom8+T19Tm2fVPDJf8SVABx9fvkx9bwIb/OsNbVHQKiDgWxeQbbfrNLcWvsx+QwINHzm3Rlb//hUcVfZude7rex3m9q8YhTmKN4Y5d2293xg4UsYY7aV92YlwT1IR2CgbJUMyNiRVppnZmBKgg48pTiZWAQG34ZV8/1ZDHA05F740VoXfFE9z8/iWN7dln4hpbu9jmlv76JxuAF/wWd93pNu3bDig36xv6w879S5pLR95z2frPYxLcm+ulbVow/tWJHcpmPtoCNDBR0OPx1aaAMbQL/etNXrSwe+NLv9ZTHN7Fl9z87fG19wim+a2dwqlbwXcbsnpO9Lj++sQePXbSnHu6u5v9h8hI9l6Lx1h0oaug4PfLFv+SNoCMONRE6CDjxohE6gMgUC+/IHn8Me6elbmqNSMWzzNTb/m9h6+5mZOc8PX3Gz4zEssLFBUBOc+wu1d0h/O/Q44dzx6/5ftRuv9A98FMofP3v/FJrkDVsmqnXjJzdLkLgVzHw2B2PySRJMDHptWBAI5ssi7KoC5ufbKjxJLEVLF09zuxNfcfq3aNLdIMASdu8e7qC+c+91w7qXPjdfW+zg5zOggHSJJlnGSiMAC+U3nv69OoiwzqzEmQAcfY6BMrkICKzzzDX1hbdp9xaTgXbyYZlptt39bi+Jpbg3+e5LL2axZhcAqG6HYuR90uMe7ULtJ7ivTuYto6/19tN7fZuu9spCTIP4iWaQj6JcnQVaZxTgRoIOPE1gmWyaBpUULjKIy96bYjuJpbtMwze2VkmlutaOY5hYJmoDPJxsGjXF7f+uKlvt/y3HumtqtgTFyqNFJOkWSNOMkEYFcydWvyOkLbvgt+CSqt1hnlQ4+1kRLT09fCq5OTV8bmWhrhAxshfSVsWVZLezwQ/HIb77hli2+XKO9sxl8UIqaLwfT3G7DNLevYjPNLRJMgUBANgwc4yn6qRPAPgDnXt6ftxvz3ud4L5A32XqPBG6SxVkmyyQg/l+TLNvMbowJlDbqJsansHxy+nBSnd1OKD9E/8O62g/QycVrpf+nDNdAc0rZPRxhOshlCbQW+hk6AFLTZ9B63vA+2osR9i4UbtHkEz/2ciukXeOah1XQbCj83GMRZuZ1Eda/gkZBpp2DFe32MzlpC+FNaH8oYgv45DfvsvLuLyJOynIRC78LSM6RGZ6cEe2K7M2u8zZ9/0tXkxkvRDWHPZJCFjv3Q+Dcf2yH6NNQ38Fvx5R58G2BUTLE6Cydy4zBHclLYKEsLFohK+YlbwmY81gQKO8WPxbpJ1MaXZFZddSVtWE44EeoD6TPlddDavoykdeg46DPITt0IfQWtA9UVatKPh/EydpBh0G/QPrrfxn0NTQEyoG0HM9A50IfQvrGOf2A+DvQ2dDbkJqWZWTxmkhNLC+FNH4/KKJXYvqXyS+e7/zuGoPsKdN6zJ+OaW7PYJqbt1XJNLdjj63wa27gFTPLHXpEUdE3LXHX9AiY/us9NmHnKcKz97e9F7H1HsYldTZ/lO83+8W/OHVKxJJUhQAdfFWo7X3MGdjUVqy21M+E7oDU1KFnQ+bz5gDWH4V0Xmp1cm+H850F6TIXUvNC90BDIb0BeQi6E7oFMh05VuV76FroCig0HJvFpp+ivB06FtJejmlQaaYtfL0xUXWD6hd+bWyrd6W0wHrSWiAf3fA6ze1dTHPr0Muof9OFNWoMw9fcYjTNLVIwOcOO8HjmNUH0xyJw7prq7YGRMoit90gBJ2E8jKB3I9vaG0dLYwLV6WisjvkUZFCfTZv2KlZ2mBtlLLUFOxo6H9IubXX06ii1/1n/wNT5fQFpq/d9SFvDn0FqZh+qOlV1lKb1xop2gZdllc1nFySkvQqmcw9Ndz42+gYD1PH+GLozuK5d+noDoDcsZZk+69u3jJ3aB3wZpHy+hZ6CtnqXySLDa4gtw4bN5LKiJXDsOs1tPqa5jRhnNHkJX3Pr0SMhhcgZcaTH82V9Qwx17uiZr9C09f6G92J5HfFpqUjgT/lTB9jp6PndqVg+lilyAnTw/7Aag1V1yqZpt3NFDl5brnOhndDvkLaMh0GmE1cH/wl0KnQp9ACkrWFdmqaOM9R5VuTxKpvPxkh/s3mysGUOtrUbPhvSmxV9ph5uGxCg++qG7wjZ1jj7hGyHrv6FjSmhAbqOO6BFRUuMjq4eFRU3/MjEbRdPc3sI09y2YprbGZNsDf5zclymuUVawpzRxxZ5PqsN5/4knHWNCA+7wzhMBhr7V27YRIRpM5oVCKD1Htgom83fICtkiXlIEAE6+H/An4bVNf9sRrSm3fO9IO2eV2sGTYb0j0sHxemvrraKVRdDR0CvQdqaXwup3QCFtq413nDdUYZVNp8LkU7rMtLScL0x0Tv95VArKJyBxtG85kFlmcZZV9bO0sJ9q+VD94/GGFePPT0ZpUVLeFjxNLeHMc3tZUxzq7Ov1D7/oozsMWMc9ip8zS2Whdkw7oQiz8cZcO5PVcK5F7feiy6W2Wy9x7IyLJbW1zJvc55s/d5i2WJ2EkAgtOWYgNMn9SnVGfaHRkBHBaUt+qOhBtCRkHbPh9ocbKjD1W7r6jI9X11ocNgJ9ebueOinYLj+IOhz9HDTMDNO+D7d1puYwyDtfo/cdsuPhZ+joWFR02lum88L+Nf1bOHxLDilsOFDb2Q1++zLrFrHHptw55571MlF7g9sATGeQZ+8dr5EavcYw+SgQNfioRCRHsN4yUQAA+swcObbXcizDqalpTkB/ZGnVUygFqKo0zZNu/K12/1TKPQPSZ8zq06HZkDToRsgfYatf3Tjof2gyjlDHBChlZZPbZ1rF/kMSB265rcO9Ai0HHodUrsG0nzpDcGzkJr2Fugz/0N1I2gZWCoL7aFoDelx26F3ocrYgqLfjIKA2xB7lnW66XWaW97dLo9vGb7mdtwpRtP3Jrgy9inr6UNlihubuBuPP91b+K4Xzn0GWuFa3ZGatt5f9VwssyLty480YcazEAFMj8PLKwoXI0v6e0NLcwJswUd2ARQPDENUHYSnuhfS7nntbg+3WQiYDO2AhkInQlugAugBSFv2mkY8rLR86nlehh6Enoc2Q+rY0fordvg+LNXWQcdBJ0CbII03Nbj9N5amDcWK5l9b3x9B6p1HQX6oMuYPeOVbzw94Gm8B02lu6wbWcW+5uLsne/w0R4tvf85scMst1nLuJ5/p3f3GLjj35yrp3BXwvcZQ6Wt0l+4WoM0sxIsApsd5Vssa7SmkkUDxjzMxxJ+AtppqQuroE23a+tYWt07bK8s0v3rzpzcl8bTx9W+0v9DgFqc+Qqh22zPNbU4Lj7NdD6lz/kVZNYYPr/ZpbpEUfNMZU7wFL+bCub+IG7PK4lSde2gAACF7SURBVPJhcEVP9zsyK6uH9IjkdIyTpASOk6NXvyNvj0X2tSeRluYE2EVfPRdAIU6jsoJtiyAT1ZXX73Z/GMirf7NR12arvm76vaa5HTbWaDxzcpbrgAMiwJKYKJsmngPnvsEP546We2Wdu+b5PjlEesO107knpgar56z6/vk/ZYn2rmkXPY0EqvWFK8RNAuEENvvz5U/vYqNdpn4bJc5W8AG+5nZ/Tbd/S8vgNLdTEjrNLZLibj77Al/B86vRcn8Jzr1eJIeExQmg9f6S+1KZieNpqUzga/kqkCc73kEZrfHcK5VhJ0nZ2IJPkopK1Wxiutwruz8OHJLZ1V6Z4eAR4wid5mar1dGGbvjM7HHj7Ime5hZJAbZccKlv1zNL/RJ4Gd3y+mSlKnafDJKexf+qcjSPSR4C78v7GzZJro6LoZFAMQE6eF4IiSXglv/tei2wqe6lRrtYdtP78Lg671bxF36Jr7n1HRxoOO3cGq4BAySW54gnuC2XXOHb+fgfUTr3AD6OMBOt9+fZeo9nZVkg7c0YE/uz/KSP336xQHaYBYsQoIO3SEWkcTbW+7fJ4qLfjXauntF30+s0t+33uDzepZjmduzJRtM5mObWoUNS4d1yxTX+nQ//Auc+Gy33RlHk/QEZKN2N3nj+TkttAp/LZ+ie3/4GSmnOikntArN0ERGgg48IEyPFk4BvlbyIwXaDXT3ttat6nvzn/ZL/dH23UdTaVnvqeY5Gxx3ndNSryjPrquYgNsdtvfr//Dsf+MEngVfg3BtHkai23p93XybTOe89CorJcuhr8moOuuf1+TuNBPYQoIPfg4IrCSNQJB/vejWwqd5lRm2bK/JWfABTwvPuDhgFOs2tTQ+pd/2FJdPcMvRdPMlnW6+/yZ9/3zdw7tpybxplAR7Eaxa74hvGfaJMh4dbncAavF16iSxah3z+ZvW8Mn/VS4AOvnp582ylE9jm3yFfFM41OmSPqNjBF/2Fr7ndga+5/YyvuQ0fYzR+HtPcevYsPeUkCd126+3+/Ls+Dzr3ZlHnukVx6/0ZPnuPmqT1E3hb3ty9TjboWzNpJLAXATr4vXBwI1EE/Ctl5q5X/eOyR9jL9G4FHwanuW1qaa91xkRpcA+muTVvnqgsx+y8eXfeHdhx60dB594iBulOwycC95MD8Y+W2gR8eOQ+S17auFt2VvZV0akNhqUrJkAHzwvBKgS+cX9t5HjXGM0y2vzTitdpbvmPBGTnrKaFtlod7HXOuzAz+4gjkmKaWyRg8+75T2D7jXO84tepcC0jOaTCOC1kOp696ydkaalO4BuZJ9sk72uUMzfVy8ryVZ4AHXzlmfGI+BDwe3PkyYLX/Q/Uu8yZXTzN7XZMc/uipS+z98BAwwcxzW3gwKSZ5hYJorwHHjS2X/cmnLsOqGsTySERxHkE7faOchD+0VKfwHR5dsNKWf506peUJawKgX+aSlU5mseQQGwJNMroKj9ldKzZ3LushS376BON2hMwza1jx9iexQKpbX/oYSPvsheLSpx7+5jlqDneOT9bHs/qX/wl45gly4QsSGCZLMPnKcd+v1T+HojslfdtCQvmnlmqDgJswVcHZZ4jUgJbfLk1F9Y665aWjaZPzHDUrx/pcUkVb/ujjxl5l8+Ec9du+dg5d5HHpK90gGvvn1Q8mNmqEXhVXsnHl+P0s8907lVDmPJH8XOxKV/FyVVAY2vBVYXvv59rr1uVj6pYv6z5Tz0leZdMLxLfLDj32L6Ap7k85b5CLuezd+tfBlHncCu+2DxbXllfJG4OrouaZuomQAefunWbrCX707t69Tfur75K1vyXme+d06fL1vOf9IhPP/ka68cOT+J9dW1lAP7RUp8AnLs7VzY+h5LuTP3SsoRVJUAHX1VyPC5uBPwrVkzLf+KJnLidIAEJ73zhBdky9WE495fg3DvHPAfN5Um03q9k6z3mZK2XYIEUyHR5Zt022fK89XLHHFmJAB28lWqDeTEJfF80f/5v7u++M7eTernzpZdky6QHgi33/eJQlmfwtbhW+GrcoDikzSStRuAteaNog2x8BfnabLW8MT/WIkAHb636YG6CBHzLl9+V/8ADG5IdyK7Zs2XLWffBub+AlnuXuBSnmTxeeIVcwdZ7XOhaK1FtvT8k09ZulA2PWStnzI0VCdDBW7FWmCclMM/zyy+/JnMrvuD112Xz6Xd6xPs8nHv3ONXqdDlAmtsOkUPilD6TtRKBV2U2Wu85LyFPSX/zayWuqZoXOvhUrdkUKBda8XfsuP/+pHwWX/DWW7Lp1FuDzr1H3GqjmTzqvpLP3uPG10oJ78R4usfl0dW5kvu4lfLFvFiXAB28deuGORP51jN//rzdn3xiJBOMgnfflU0n3eiRouloucfzIzjPSw9pIkPwj5b6BJ6VZ3evkdXPoKS5qV9aljAWBGyxSIRpkEAcCXTJOuSQD5t9+mkbW2ZmHE8Tm6QLPvhANh1ztUc8z8G5x/djL03lQPeLcm/WUDk0NplnKpYlkCM5MkpGLFoii/WtdTssm1FmzFIE2IK3VHUwM6UQWFy0YsXbu15/3VvKPksF7f74Yzj3q+Dcn4m7cxd5EU/1G6HtPtRSDJiZ+BB4TB7JWymr/4PU6dzjgzglU6WDT8lqTa1CBdatuyv/3ntX+rdssWzBCj/7TDYedTmc+9Nw7vH/0EtTmVZ4FZ6924SdcJa9KGKUsd/kN3ldXl3kloKXY5Qkk0kTAo40KSeLmdwEdvndbnx1zT+4xvDhlpsOVvjll5I79kKPuJ+Ec9ce1Hjby7iF+Mt/vdzgpIOPN+vEpu8Xv5wv5+T8IN+fjZysSmxuePZkI8AWfLLVWLrmd8eO5zDt7I+ihQstRaBw7lzJHXM+nPtjGCBQPS+aaSIPFl6Jee907pa6FOKSmXflHd/v8ttHSPybuJyAiaY0ATr4lK7elCqc1/f331dvu+aa9YbXGo/jdY5+7qhzPVL4CJz7kGrqK5+N1+XUsQ2T4SlVuSzMvwlskk1yu9y6Yq2svfHfexlCAhUTYBd9xYwYwzoE1gZ2727jqFevl6tPn4R+6tj9ww+yYfgkOPdpGSLDq+1GuYlMdv9Hrs/qGPOP1VinkpmTEgLXybXbvpAvrvdK0ddkQgJVIVBtP0xVyRyPIYFwAoH162/d8cADf3tXrgzfVW3b7p9/hnOf6JHd98O5H1aNf0NvyH5SE233w6qtrDxRYgjMla+MD+X97wtk58zE5IBnTQUCbMGnQi2mVxkKAzt3rvHn5AzLPvroWjZ7NfpXcMbrcyV36BkeKbgPPQijqvXvp7FMKvyP/F9WJ+lUTY8D0uvCskppt8k2OVPOWLlYFp2GPG21Sr6Yj+QjUL2/jsnHhzm2IgGP5+OC9977a+czz3iqM3ueBQtkw5DTPcaue+DcR1ercxd5Cx+ZrWE/TEbQuVdnpSfgXLfJLXl/y9J7cOqlCTg9T5lCBOjgU6gy06QoHVDOp2TXrh/yp01bUvTnn9VS7KI//pANg06Gc78Tzn1cNTt3kcbyH8x7vyLTIdV+6mrhy5OUEPhIPvC/J3Pm7pQd+kpaGglERYAOPip8PLiaCZyC8/0X0hd+XO1dsuTCbZdeui7gdsc1G0VLlkjOwBM9xs5b4dyPTICHfVc6SaZ9pBzO1ntcazqxia+RNXKtXLN0lay8EDkJJDY3PHsqEKCDT4VajE8ZJiDZr0tJWufjnh4M/xDLQig/TFcH95uLaVgpgOqbAVhqGuZx2tVeFLI9FevhdiQC+kBToE+DO+fhYzSPb7/11ri9vrPor78kp/9xbiP/Rjj3YxPg3AUvpL3HfbVcydZ7sNJTceEVr1wml+Yukb+vRPnWpmIZWabqJ0AHX/3MU+2MV6BAdcKkzw9Ng2MUdc4vQqeagVjOhMzjHsX6UyHbuh5uyxBwObQ5dEdg8+b/7HrllXl4Ju8PDY/FetHy5ZJz8LEeY8f1KMOJCXHuIh+g9e6wHS6j2HqPRaVaNI2H5aGCH+S7WX7xvGfRLDJbSUiADj4JKy3Jsjwa+f0VehKaHEXeF5VxrNe/cuU5eddd96d3xYoyolQ+WKfh5Rx4pMfYfjUc+8l6k5IQayh34nvvV2Tw2XtC8FfLST+RjwNPyGPf5cqG66vlhDxJ2hBI2A9X2hBO7oLug+zfH1aE9mHb+jHy8BvFlxC2LRjvDCxfhX6BsqG+0M9QLG2dd8GCc7dMmTKr6ZtvtrLXrRtV2r41a2R9v/FuI+9y/H2cnsC/kQ/RerfbRsuYcL5RlY8HW4fAclkuePzy10pZMRm50sddNBKIGQH+cMQMZcompNdIqMIL2hkBw8NUKxipXjB8TnB7Npb6QxYP+9r96693b73qqq1GoOrjk4qde99xbmPLRXDsZyXQuYs0KG69X57hlIRmIx51xTRBYCf+XSjnrftDFp6HzdWEQgKxJsBfjlgTTa30tM/7krAiHRi2rc/L9Rl6aXYCAmtC2kWvpusqfZZeAMXW8vIe2/3BBwfkP/LIqXUvukh7CyplvpwcWX/gOHdg8/nolp+S4L+Nj6WDBGxjZCxvwitVi8kRWb8Sd4lcvOUH+eku5PjL5Mg1c5lsBPjjkWw1llz51e75a6CjghqB5VroeCgeZuDb8ZfkP/jg3F1vvumrzAl8ubmyvs9od2DTOXDuZ2dU5th4xNXW+1VyuZOt93jQTXya98jduz6Vj2flS95jic8Nc5CqBOjgU7Vmq69c2lJuECbtou8A6bS2Z6BFIXoR65OheNlu38qVZ+b93//95v7++4jO4du0Cc59lDuQOwnO/byEO3eRz2Qf8drGyhHIDy3VCMyUF4qmyzOfbpCc8OmkqVZUlifBBOjgE1wBKXD6e1EGfV92qHSQnc5z/xjaCYWavqRmALR/aGCM1zf6/vrrjK3nnPOn9++/y03at2WL5PQ+3BPIOQN/CxdZwLnr3dJtxa33DLFEdsrlx52VI/CRfOi/XW75fpWsmoQj4/uGpspljbFTkADn1qZgpbJIewj0y+zX75Wmr73W3tmmzZ5Ac8W3bZvk9Bzh9q89Cc79SnzT3Qr2Jbo9rvXga2KuTLFIlqyAJQXy8JP8JBNlwq9/ypIjUZy1KVAkFsHiBNgFaPEKYvaiIrDev3Hj4qL584dmjx1b115Tx/eVmH/7dsnpBee+5jg496st40nry0T3HehJ6C492LtmVlYKLH+X39W5L14ii09EcXTwKo0E4k6APyJxR8wTJJSAz/epe/78yzdPmLDejxa7WiA/X9b3HuHxrz4GPVjXWsa5i8yV9lJoG5+Q990ntJZS+uQLZIFMkjOXLpI/TkFBq+frSClNlIWLlABb8JGSYrzkJVBUtNi3YUMuWvIDsoYOrb3+4HFu/0qdfnaDy0qFqisTPXfI+Rk95AA+OrNSxUSRly/kcxks/WWj5B6KZBZEkRQPJYFKE+APSaWR8YCkJdCgwYmyrfAVTMPHx21us5RzF5knPeUyzzz52uUSi2UtaSs8sRlfJsvw8YWTlv0i8/WZ++LE5oZnT0cC7KJPx1pP1zJv24Y36bXsKvIjRvxvtBSFunKz+wq51EnnbqlqqXJmFsOfw7kvhXM/DYnQuVeZJA+MhgAdfDT0eGwSEliGH9uvLxI5aZ3IBovk/zs8e8+3HSlH85GZRWokmmz8jE8tnCYn/wnnrl9P/CGatHgsCURDgD8o0dDjsUlKwLcEzh2Dnb4ZKDIY78tvmNBy1JFJ7lvx9rxe0ps33AmtiehP/o3MM6bIxIWLZdEJSO336FNkCiRQdQL8Qak6Ox6Z1AQ8H8LBo/v0eLwJ58cEluQHtN7zbEfLMbzZTmAtxOLUr8ur3sky8UfMc9dXM7NbPhZQmUZUBPijEhU+HpzkBNbiWfxnGODWX2TfJni7brXf8NYubr1PdfaWPtV+7iSvO0tl/0F5cPddcvsXK2SFttzXWypzzEzaEqCDT9uqZ8GDBLaIbHlf5LteIrbmIv3wftjqmlzys3SWV/33yX2ZfC1tcl6PhfiE+11yx7bH5bE318qas1CK7clZEuY6FQnQwadirbJMlSWA9+XnvYVpyq1FVrUVGVZDquEb7LUx7/1mmeLoK33Zeq9sjVkgfq7kylkyIfcdeevBXNmgH47B9EsaCViHAB28deqCOUksgSJ8F2eOyHIDrfku+B5OHRGMv4ubaet9NlvvceMb34T1vfJnyRnLv5NvL8yXfP1iIq4bGglYiwAdvLXqg7lJLAH8SBd+g1eF4/O2n/UV6YTP4O4Tl/76WjLFc5NMcBwo/dh6T2ydV/rsz8mznqvksvkLZeEpfvF/XekEeAAJVBMBOvhqAs3TJBOBwHIMvvsAo+x7iPgx+K4P3lcfyz+V36STvOT/r/w3k1+MS57rIk/y5Eq5fOuz8vRbK2WlvsBmTfLknjlNRwKx/NVKR34sc+oSwGCpvNfwXL62yLftRQ7Csn5MSltTJntukNMdB8nBbL3HhGj8E/levtcu+VVfyhe3bJJNN+KM/JZ7/LHzDFESiEv3Y5R54uEkYCUC6oSfE2kzROSBViLHOKPL3ALpKhOK3zlfS2pFlxSPjjuBIimSJ+SxgsfkkV+Xy/JzccI/4n5SnoAEYkSALfgYgWQyKUtApz5lieyYjLeOthFZ2FTkQHxYHg36KlhNmVJ0vZzqOFj6s/VeBX7Vecgf8OX4zOv6N+XNZ9bK2qk4N+e3V2cF8FwkQAIkEEcC3ZH2m1DIcPoaJ4r0wGtu3/Bi4DQG5VVGC40u0tOdJzsNL46jrMlgl7iNafJwQUfZ9yfU/YA4Xl9MmgTiSoAt+LjiZeJJTACtdLkvKAy6M82HEfYb4fR/aIFX3KI1fwD62THYPgJD691znZxs7y8D+HcXAa9ERPlOvpPJctbat+Ttp9fKavTaSEjdJyJHPCcJVJ0An8FXnR2PLCGgjlDnAO9OMiAV5fu4YJkwmr4sc4wW6XinyNR9Rc7LLu7JLyuqLJH95WTPN/jee+0qdu+XmTR3RE1gIz4fPE0eyJsh09dulk36cYIpUSfKBEiABEjAIgTwfLnYUZfWuuyHfXil6152Prb0U5iLIW3lLIRGQKa9h5WLzI3gEq3e4nPgmba8AeUHFcCyIGS7Mda/hD6FQk0/+6Y3E2YeV2P90GCEL7GsKL5GrSjfGqcyhhZ8kxtE+q8QmeMXCZTabZ8tR7sflkeL2C1vrW75AvEYT8hT7m7SfQluvCai4nUQ5VNQ6LVcmeuBcUmABEjAcgQq4+BvRu6/gPDu9j2GD7YUv4e7bTCkIge/50CsrIWGhAZg/UuoENJBbqZV5OArin8zEqoo3+a5KrvsKNL2RZEjMRDrVwP3ISH609hPeni2yg4+d7fI2IMi3Ii9Jx8GBsuQVc2k2d2o7EYhFd4J6zr2omojKUMS4ioJkAAJWIFApA5eWzjaHQ+H9i8bhxBMJSu2WDj4W5BSLoRn3cVWkYMvL36k+Q6eqsqLQUDzOXp4N6Fzo9jJ10DrfZo8wta7RZz7XPnWGCvj1rWVdrNQy/uVUdMXIPzKMvYxmASSgoD+6NFIoDIEuiFyEbSslIPUqYfa0dhoFxKA7uxKGQa0yQzoYegEqCIrL35l8l3RecrbPw9oDoPwfP7L60UGdm4hf9Y8VaZnlHcQ98WfgA6ge1im5c6Xn39ZIctvxRn1EVNZ9jR24NGLtIdWlhJJfzvrQNtK2ccgErAEAc7FtUQ1JFUmWiO3W0NyrIPVfobmB3VNyD4b1vUaC1XI7ohW9YdYxwAcGVFskbLiVybfEZ6qzGgB7HlfZOlg3J/cYYhv8Y1y3Ra8u7zMA7gjPgQMDNn4Bv9OlOM3nCmnf/CazD4Bzn0szlaec9fM6JfhcIP2L+feDmFzoB3Q95DeVN4E6bWuVtHYkop6tvQmGD0/shMyx6iYSwzmLL6p0P14FFT8aEsfb62D8DXEPT1dWKWRAAmQwD8EIu2i13jqwMzWuLZktKWu0h+3RyG1in7ISmKV/F/WM3iz1X4EoukP2j6Q/riVNciuvPiR5hvJx9w0v2M7SqevzpAzN3wucw19BswBd/EbcLdbiow35G3/CBm5poN00OtyYAxqVW9mV0Pq+JsF09M5kl9AM4LboYvSruuK/i5MB984NKGQde010L+BRiFh9bH+LvRsSBhXSaAaPnpNyKlGYA0KhOfLchT0IuSDtPWgpi1t/QGKtWmLaRJ0b4QJlxY/Efk2s+vHyvvLZOmH0OBvZd4FLaV1v3PlvJaHyygHX1lrYop+qd9of1vedL8gz2/AN9o/xBvonkCqC6NPuTiFc/D/Muj2kPS0i15vbrdC10IboOq2PJzwG0j//mgksIeAtr5oJBBKQFsk6pBM0+7BcJuIgNmQtuS1RaLLUdBp0EwoHnYREl1ciYRLi5+IfIdmWTl9tUyWqTovlaUT68vNRx0lxzQ9QU6q20W6hMbleoQE8MlWPF3/Dt1Hr23+VD7egDntz22X7Xp95kaYRKTRuiHij6VExoeJih1/Hyz176Ei0xuCdiGRzN6wkKDiefg7QwL0RuKlkO0hWN8B6W+4dt1fDelNMI0E9hCgg9+DgitBAto6DzX9IXGHBmD9A0h/TC6HtFswA9IW8h3Qk1A8TNO/BYq0FV9a/ETkuywWf+XIuquh2/+UpaPxbHgKRnXve5Kc2mKkjHRi6lZZxzE8SAA3SfKhfFD4iszauFW2/rBclj2HXV9COgg0Hqbd5n+UkXAOwluWsS88WJ/X20MCQ9fN4MFY0XEApq3FSqiDvy24oyOWv0PDoN+CYVyQAAmQQEwIZCOVujFJqXoTsWK+92soDa/uJPv+dqqcvu5ledWfI5v4rD5ket0KWWM8Jc8WjZTDV+8rnedlS62zcdk0r6ZL5y6c58EyzrUc4QeF7VOnrDfIoaYtfO1dCrXQF0BV9hl8DySkjwd0nAqNBPYiwBb8Xji4UQUCu6twjBUOsWK+/0RLVHXfUvm7z2fy6dg6UveoXtKr/uEyujneYe/sWOrrB6yAMz55CODpz2L8mytfut+VdzaulXUbNknubHTBf4gz/hWfs5aZ6nfY8zCk3eGhret+2G4FaUu6um0BTng9NAPqCsX6sQSSpCUrAe0qopEACViXgHbfHlBPGhzaVJqMry/1242WMfX6yUF1e0ovDKVuZN2cVzFnGzBO7Sc86v5KvtwM7SqUwiVrZPUbbnHPRZI6yC1Rpr+Xr0M6RmUqpM/eu0OvQdOgx6FQ0xa8jkv5KiRQW/CfQA+FhGkLfj1UA9JGlz577wxtgUJtFzayIH32ro8LzP2arx+hxdAEiEYCxQTo4HkhkEByEdDu6APbSvsRLskYjOf2dfvLwPq9pTcG6XXFW1naJ1VpivC4XJ+l/43G+LfyzRZo1w7ZsXGH5H+2QdZ/jsL8DKlDs4rpVDmdCjoSyoB0BPszUGljQ6Jx8EjyX6Y3C3OgcAevEfXxwLfQIEh7GmgksOflDERBAiSQnATUo3drIa0G1JHaAzIko9X+sn8GWvf1u0q3Wq2lTfG/uhYYJpEHX7gc/1bLKnXoO36UH3fAtRcZYlu6XbZ9i9Hv36Ms2uW8KQmqQhtHDSB9/k0jAUsSYAvektXCTJFAlQloN6+OrO7YUlr2qiV1MAgrsA8Go9XaVzo54PCz20n7OtiXWU/Q4R/81wC+Kqu497fy59Xn5DuD/9D6FnxuFX3H+m+zG1MBd8CpF8KpGz7x5QXE+HuzbP41T7YuwpmWQTo4zVf5s/IIEiCBigjQwVdEiPtJIDUI6EyHVlBTqFkTadahjtRq7ZRMzMczGsFJ18NrXbOyJRtt/bqGS1x29Abgn8uujt8veO8e/iGe4cEnVnfLbgPPxDHSzBPYJTttdnEU2MWuXcdbisSzDq31Vdtk2zps66Avfb6sKoRoJEAC1USADr6aQPM0JJAEBPT3QJ8xay+AOeBLnzM7IDV9UY9KW9w6ilzfj6AqgGgkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIk8P/t0LEAAAAAwCB/61HsK4QMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABAwYMGDBgwIABA89AOFIRYC1HspgAAAAASUVORK5CYII=" alt="plot of chunk unnamed-chunk-5"/> </p>

<h3>What category of weather has the biggest financial impact on the US?</h3>

<p>To answer this question we summarize the damage to property and crop and aggregate the summarized data based on the weather type.</p>

<pre><code class="r"># utility function to aggregate the cost per event
sum_damage &lt;- function(x) {

    if (toupper(x[5]) == &quot;H&quot;) {
        mul_p &lt;- 100
    } else if (toupper(x[5]) == &quot;K&quot;) {
        mul_p &lt;- 1000
    } else if (toupper(x[5]) == &quot;M&quot;) {
        mul_p &lt;- 1e+06
    } else if (toupper(x[5]) == &quot;B&quot;) {
        mul_p &lt;- 1e+09
    } else {
        mul_p &lt;- 0
    }

    if (toupper(x[7]) == &quot;H&quot;) {
        mul_c &lt;- 100
    } else if (toupper(x[7]) == &quot;K&quot;) {
        mul_c &lt;- 1000
    } else if (toupper(x[7]) == &quot;M&quot;) {
        mul_c &lt;- 1e+06
    } else if (toupper(x[7]) == &quot;B&quot;) {
        mul_c &lt;- 1e+09
    } else {
        mul_c &lt;- 0
    }
    (as.numeric(x[4]) * mul_p + as.numeric(x[6]) * mul_c)/1e+06
}

storm_table$&quot;Sum of Damages in Mil $&quot; &lt;- apply(storm_table, 1, function(row) sum_damage(row))
ev_table &lt;- ddply(storm_table, &quot;EVTYPE&quot;, function(x) colSums(x[&quot;Sum of Damages in Mil $&quot;]))
</code></pre>

<p>The following table displays the 40 weather patterns with the financial impact in the US:</p>

<pre><code class="r">s_data &lt;- ev_table[with(ev_table, order(-ev_table[2])), ]
s_data[1:num_rows, ]
</code></pre>

<pre><code>##                         EVTYPE Sum of Damages in Mil $
## 170                      FLOOD                150319.7
## 411          HURRICANE/TYPHOON                 71913.7
## 834                    TORNADO                 57352.1
## 670                STORM SURGE                 43323.5
## 244                       HAIL                 18758.2
## 153                FLASH FLOOD                 17562.1
## 95                     DROUGHT                 15018.7
## 402                  HURRICANE                 14610.2
## 590                RIVER FLOOD                 10148.4
## 427                  ICE STORM                  8967.0
## 848             TROPICAL STORM                  8382.2
## 972               WINTER STORM                  6715.4
## 359                  HIGH WIND                  5908.6
## 957                   WILDFIRE                  5060.6
## 856                  TSTM WIND                  5038.9
## 671           STORM SURGE/TIDE                  4642.0
## 760          THUNDERSTORM WIND                  3898.0
## 408             HURRICANE OPAL                  3191.8
## 955           WILD/FOREST FIRE                  3108.6
## 299  HEAVY RAIN/SEVERE WEATHER                  2500.0
## 786         THUNDERSTORM WINDS                  1926.6
## 842 TORNADOES, TSTM WIND, HAIL                  1602.5
## 290                 HEAVY RAIN                  1427.6
## 140               EXTREME COLD                  1360.7
## 604        SEVERE THUNDERSTORM                  1205.6
## 212               FROST/FREEZE                  1103.6
## 310                 HEAVY SNOW                  1067.2
## 464                  LIGHTNING                   940.8
## 30                    BLIZZARD                   771.3
## 376                 HIGH WINDS                   649.0
## 954                 WILD FIRES                   624.1
## 879                    TYPHOON                   601.1
## 130             EXCESSIVE HEAT                   500.2
## 192                     FREEZE                   446.4
## 275                       HEAT                   403.3
## 405             HURRICANE ERIN                   394.1
## 442                  LANDSLIDE                   344.6
## 164             FLASH FLOODING                   322.9
## 161          FLASH FLOOD/FLOOD                   273.0
## 87             DAMAGING FREEZE                   270.1
</code></pre>

<p>To summarize the most damaging weather patterns in the US the 5 highest ranked are display in a pie chart </p>

<pre><code class="r">
slices &lt;- c(s_data[1:5, 2], sum(s_data[6:nrow(s_data), 2]))
lbls &lt;- c(as.character(s_data$EVTYPE[1]), as.character(s_data$EVTYPE[2]), as.character(s_data$EVTYPE[3]),
    as.character(s_data$EVTYPE[4]), as.character(s_data$EVTYPE[5]), &quot;OTHER&quot;)
pie(round(slices/sum(slices) * 100), labels = lbls, col = rainbow(length(lbls)),
    main = &quot;Financial impact by Weather Pattern&quot;)
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-8"/> </p>

<p>Note: This date is somewhat skewed as the EVTYPE needs to get cleaned up. As an example &ldquo;TSTM WIND&rdquo; and &ldquo;TSTM WIND (G45)&rdquo; are considered different types. Also there are EVTYPEs like &ldquo;Summary September 4&rdquo;, but since most of these have no impact on the results of this lab they were left in the table</p>

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