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en:learning:schools:s01:code-examples:ba-ce-10-01 [2015/09/22 16:21] (current)
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 +====== C10-1 Visualization of spatial data sets in R ======
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 +<​body>​
 +
 +<​p>​The following examples illustrate the visualization of spatial data sets using
 +the sp and raster package.</​p>​
 +
 +<p>As always, we start with importing the packages and setting the working
 +directory.</​p>​
 +
 +<​pre><​code class="​r">​library(sp)
 +library(raster)
 +library(RColorBrewer) ​ # Necessary for individual color maps
 +library(latticeExtra) ​ # Necessary for adding a layer to plots (see add.layer)
 +</​code></​pre>​
 +
 +<​pre><​code>##​ Loading required package: lattice
 +</​code></​pre>​
 +
 +<​pre><​code class="​r">​library(grid) ​ # Necessary for plotting the raster grids
 +library(rgdal) ​ # Necessary to read vector files when reading with sp
 +</​code></​pre>​
 +
 +<​pre><​code>##​ rgdal: version: 0.8-14, (SVN revision 496)
 +## Geospatial Data Abstraction Library extensions to R successfully loaded
 +## Loaded GDAL runtime: GDAL 1.10.1, released 2013/08/26
 +## Path to GDAL shared files: C:/Program Files/​R/​R-2.15.2/​library/​rgdal/​gdal
 +## GDAL does not use iconv for recoding strings.
 +## Loaded PROJ.4 runtime: Rel. 4.8.0, 6 March 2012, [PJ_VERSION:​ 480]
 +## Path to PROJ.4 shared files: C:/Program Files/​R/​R-2.15.2/​library/​rgdal/​proj
 +</​code></​pre>​
 +
 +<​pre><​code class="​r">​
 +setwd(&​quot;​D:/​active/​moc/​dm/​programs/​r-dm-ln-12&​quot;​)
 +</​code></​pre>​
 +
 +<​h1>​Visualization of point vectors using sp</​h1>​
 +
 +<p>A LiDAR data set is supplied as a simple xyz text file. Preprocessing of the 
 +data follows the previous examples.</​p>​
 +
 +<​pre><​code class="​r">​lidar &lt;- read.table(&​quot;​lidar_example.txt&​quot;,​ header = TRUE, sep = &​quot;,&​quot;​)
 +coordinates(lidar) &lt;- ~X + Y
 +projection(lidar) &lt;- &​quot;​+proj=utm +zone=32 +ellps=GRS80 +units=m +north&​quot;​
 +</​code></​pre>​
 +
 +<​p>​The Lidar data comes with a classification attribute, which hase the following ​
 +values:</​p>​
 +
 +<​pre><​code class="​r">​unique(lidar$Classification)
 +</​code></​pre>​
 +
 +<​pre><​code>##​ [1] 13  2
 +</​code></​pre>​
 +
 +<​p>​Now one can plot the entire data set as maps by just using the spplot command.</​p>​
 +
 +<​pre><​code class="​r">​spplot(lidar)
 +</​code></​pre>​
 +
 +<​pre><​code>##​ Warning: non-vector columns will be ignored
 +</​code></​pre>​
 +
 +<​p><​img 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WnMLprOIZHfI/​5Ss0s/​npqFbGemdrmvG2m58d3c9biyksFXp0rtqtQsAF9alZqVAh7UhOLB39epSu2q1Kx7AF9YlZoF4EurUrPWBH9DmprLXKcLtQt/​7Bdv728fvbLuHrffPT38rbF33Dp3QJJZOY24vbiiO+PsWtfjb798nutUgXZdPxgr6MUXbx0VZdmVSH3OrKxG3F7gs20L/​N3TbA4fCP7Dr56Tz73CFXV7cThcEffDVUefbh/​98fHhsx/​GvR/​G5+e0zPjywU90Ey7y1e/​Icclm5TXi9tEfHlLw9FR3z34/​BtIbEkxpWatdq4K/​HsNdLoXZdcM/​cawoXP/​46ZtX99cP79kTjack1l6Npf+PlmFR9sUlPsHtxaU8T4pZeY0Yt724Uk5193Q8wQU5CS47ns5q15rgb2auxiQFgufVwELjWNPU/​5gbygqWsXMsQ99jLGMx4lyhkdUKPqsReNu3b+Wp7p49v8f/​x0L4dLi8za4VwX94HBovQxRm1+1X0tnGZpYG1gscTtmTrHNWkpSRGMYqXQw+rxH48fpSnkoF/​3hMIj+zplFrgn9hD0KJim/​j8YXHALLge0PyLdXZWJm8Hp/​XCHol/​CBOpYK3ezuxaz3wwc1kmOKzelJ3Xz7Hdoz/​2ZPWvN4++omW0ZvXpeDzGkEecWrPTqWAZ2Wtdq0GfuyoYmXz+oR+/​PXh8Be/​uWKxlodc4Wk0oaZl7p6qCfVi8PmMEFEBX0usmAKeHm61az2Pz6xK7arULABfWpWalQIe1ITiwc/​tWFmV2lWpWakrcHpdZIu5FZlvzKNkKbJ3etLJWfiGruvJI+qIpF22FXP6YkDn8rnparqjelJlWd7kPLYTu80yzjtdxThvkn64PGmYXQvA2wieVyP6TorbZauqNVWvWWng16Y+Cru8Tr7SGz0qNWu74DWPp+DXrk6LAHxudboAfJQ2Cx6zBo9P12bBEwH4ZG0WPPH4SXK3dnVaBOCzc7eE+ipruFKztgkey5LcVVnDlZq1SfDdVAhCfYw2Cr43GngAH6ttgu8A/​FJtFPwEe7XgEYDPzR7AL9EWwZs9eACfoA2C7/​hU/​CbAQxufjTunD+CXaHvgZ7gD+DhtEbyFOoCP1fbA27ED+EhtDvwcdwAfp+2At6byAD5VGwFPe+4u7rWCRwA+nTr517u5A/​g4VQ++s87IAPilqh0883U/​91rBQxufhl0S97EH8FGqGjy9V8abzyvgq6zhSs2qFLzi5iHYAXykqgXfC+wB0CsO9fVej7WCD0vpqgcPHh9JPdzVIdSnqFrwkdjB4yNVKXjKEkJ9OVUKng3cRIOvr4YrNetM4Du+Ysq2x749OtbXW8OVmnUW8JI8ZiQ3soe+6yblk8CvXZ0W7Rk8hS18WP56Td+rm43iAL6kzgDeiUvbzw/​ok7P6Kmu4UrNKg3dzn1wHLKvzHTgDfu3qtGin4Lu5sRhr+saulGhvB/​DRKgheaadn3FeddPWVBfB5VdLj+ayqi2Yi5Tnw9dVwvWaV83gNcAThtIuh3hqu0ayCHq+04vnc2gd+7eqcaj/​gO/​6LVKJLltpqA/​iCKgBec/​DUHL0Z8PtK7rpzuTmAT1VW8HzYRXFzdhmcC3yVNVypWXnBd2KlnByZB/​BVmpUTvDoBQ6Cfzd8h1EcqI3hOXSyFP19iB+CjlQ+8OrESvUQWwJ9bOT2+l5E+YVoVwJ9VGcHLPwnWJayOzgO+yhqu1Kyc/​XhCoNeG7c4Lfu3qnKre6zEfePNWt/​OiB/​BRygi+WyebB/​BJyu3xchDnXGk9/​5hKwe+gje/​4Wmlj6O483AF8nPKBpxh6ZV72vEJ11nClZuUeuUtZHpsufpH1Avza1TnVPsCf1deVMFMx+PZDvfgh+XMO1irXG4CPUk7wYszuXOS1BBLARykreHWSphzt3v4awEcpM/​hcY3basr3eusccJwLwUSrj8cslegnaXyLplffGRdZXCh7tA7y55G4Re47dsp2TV/​wfwEcpFbzT5YOWYbhLMLJaGBEuLkeL+LXRM/​D11XClZpVK7kJc3tcy8B9QUX1b3IjXizW9fd3gd9LGy3tnwoL9PHt2Osm9N3byowF8kkqBD4n10on1wnLYX7BXtnfaXr7sp9o2fj/​gwwbsrT+Co7JXC/​C9vXIsTwCEAHyUcoMXk/​GhoV7rlSlrt/​jlI+NIb8SCTiePqq3hSs3KDV5ffmWDzR9EiBCXTMeX6nYTPxeD8jLj6zWvB/​BRygxeuXsuwN07uRxbrsvWczYJ3HB5w98BfKRye3wIcA0i52+gFeGgVy6GjjcjFu61tvH1Xo/​523jnJI0yj9P3nfizI+o10wvynXJSI6xMPhjARykneOmWHsfXJtjE+ky+ak9eFxJ6ZyEN4Jcoq8d3wjPd4JXAzrmKITmxZ0K6U6+OzYBvvo2XWba7Lydp98qfl5LtvJa0K9BlP3FbHt88eMnN04vvldAuh92VcR9+s/​U8YwC/​XPnAc2adb8RW8hZOr4QJ0dJHcq+1O1epWbnAi+S8E73xee5yZM6ky5oAecpY8GtX51StgzfjuOHi+lSM0V9TGnztNYAvqIzg5Q8a8ougV9+YvTzzDPQ0xobtg99DG88SPIFfnUPnibk24J5VAD5KWcCLMTjL0hslGNCyakTQzwHgz6hM4HnvTLq3mdBpQzXODjmAP4eygFfa9t7m+ayMyOZtbXwe8FXWcKVmLQLPx+tkOie2y5ROzrLJKyA/​efD4KGUAL8faFdbS19VEX3b3IdSvrIXgxZSJmsz1Yoym112e9/​DEbe0AfjUtBS+pyrGXTg/​+7CoQoYAfkatLx6JHpeBRneB9fIPAixF62XtXl971olnvZZE+C3a1fam3hms0K4fHqyFeDMVKv9Y6el2Xw9nFDI962kpruFKzMoHX/​ouN8lpQu3GyuV8sPjAkwa9dnVO1Cr5TkGs4Jxm9UjgHdTEhxENOpeBbTe46ORqnTK3pi2mMhD9bTqd0FwF8rBaDF1UvB27F5KwM/​mJwRw7qLCAuQonCHUbu4rTc4/​lDr6Awhm20tjhHpJengOQuUcvAq9m1Fnx7E7rmoguhd/​JVZ4BfuzotahJ8ryyL15xZ2dDJ8R3Z7qdTl+mEnkAA+DgtBK/​lbpKnEonFbE0vC6W7vNJb1DrxEOpjlQO8NiTLsfRaM9zL8fk06mI0QH0N4NO1GLxs0eVNTwoXxT214LBQJnUAH6vl4PspTO7haprXy5Y+ibPSd5hE+Zrb+IbBUzTaK85IGbTrxZbYvF4J7/​q1sAnwzSZ3vRg5lUAMR+3UZnlBI28HXnmobxm86MCrb8xJuZ5F+8SBO3HxzJKvFHylZuVbbGn4phi1Z7vS3b0TT0obD+CXKg9462XA8zn1Tlg1NARy16+peaE62/​g9gee5nfivrKtPFL9kAHwuFfF45b4afRAveczO5eoQ6lNUxONl70skdHy2Njmnt3XdAXyyfHxjwHfKc6e+UW6nTGMepEpruOnuHCfEu2x8vk4Fn7iKvlOmeQLAr12dFrUOvrPQmtsfxZ0LwGdVRo+3UJOXQTRwua4qMNijamu4UrPKgecQo7H3iqtvvY2v1KwC4BcttNChRwlBqI9RAY+3YI+cjdO8PaQrB+CjVQD8EoePCO0bAY92BH6JUrFXC35PHp8Be8IVAOCjVBf4sKEaAJ9BFYHvxJI8AF9eFYFX2CeDr7KGKzWrIvCpzAF8vGoCv4g7hPo41QQ+g8evXZ0WAfgQ8NCPP5fqAb/​I3QF8rAB8aQH4c4CvsoYrNasW8EvJg8dHqRrwSx0ewMepEvCLsUOoj1Qd4DNwr9Xj670eKwCfg3ut4MHjzwK+yhqu1CwAX1SVmpUMPiv5TNyJXWtXqKFKzUq/​d6428tKutatUU6VmKXbFg69QldpVqVkAvrQqNQvAl1alZqWABzWhaPCgtgXgdyoAv1MB+J0KwO9UAH6nAvA7FYDfqQD8TtUceDYrpejd4S//​jND7X7zGb96Q4awn7AlveX9x+NlruuNnf/​qebtuBWgMv5iOl3v0cPxC6bAO+EPjTx1//​iN6Mr15S4i8B/​CalzEQLYfAvP//​PX3DwGLV8ev/​LP6OP//​D602/​JOwC/​Uc2BF6F+1Jufq0/​M4z8+pqEfwG9Uc6FegtcdfnzxGOcA7//​qR4S9HsBvVZbkTgevtfCIEH/​H2v+ROoBvRwb4l1+rT+QC4N4P4JuSDp5lcTyZ4x6P+X/​6t9cAviFJ8Pi/​1sTjDe8Oh89/​JP14/​ATg2xEFT/​S/​rx3liAB8O6Ijd1if/​sNT9BOM3IEaV3Pgh2G67c3X7iF61gCQ/​E9s+PQ9KUcP+ZfmQkFr4IdhSh6PyiL3ED3ZQYCLDW8k6nd6wSbUGPhhsJCnzJxD9IgP58tu3t/​8I0dNDgHwVcsGfuSLn5xD9ESkt8c2fPrtf38vfB8fAuDrlsXhaaR3D9HTgrirzza8+foTB08PAfCVa9rE00bdPURPt4lR3SfjxSLA00MA/​OZEPd49RE8LSvA0l1cPAfCbE2njPUP0dBsewBUbuMezQwD89oSZuYfoqa/​zfvzn9BoZwSuD+wB+e2L9eJf2N4i/​B/​Bjiu4psMNB/​F2AB00F4Heq5sCfTtNtJNQ7pmD4rA1+8Y5P2oglO9+P2R6E+tp1Ok3Jk+TOPQVD8/​2xj4dxv9HuwBgPgUma6nU6WchjZp4pGLZOh9LF8MUdGGykH8DXLRt4hs45BcNK0mvgjbo68/​0v/​73JxXiNgbeFetaNd07BEH36nuQCF3QAh4O/​eELOAOAr17SJZ/​MyzikYrI+PWXdfDu8hNnH/​6/​ZusWkO/​FSKxxPZpmAQdW3ES8jyH/​8JwG9UShs/​OwUjuMtZO36hvIRQv1XJXH1uCmZ8xXv0ooS4A+Ox3hFsRHsAD5M0Fu0BPEzSWLQL8KCpAPxO1Rz4vp9uG0O9cwqGvGK76OpbfqMNK/​MEQn3twn/​2yNw2Jne+KRh6JYy7cLY/​PvEbbYjetPjjOI2Bp3/​wytiI4XqmYKjGXXjD+MRutKFbyYQOgK9aNvAcoGsKhmrcxUZoFY9nEzoAvm5ZHJ51451TMIjv4iN3rKlHiE/​oAPjKNW3i2UIL5xSM2MU2yBtt+IQOgN+ciEd7pmDELtbGyyF7PqED4DcnwtIzBSN24QYBN/​XKrXUIPH6jevkEeaZgSF+O7mKN+zvlDhsAv1HBJI1FewAPkzQW7QI8aCoAv1M1B77rptvGUO+cgiHPrMTkZ9DIdgj1tavrpuTH5M43BSMmaSY/​g8bndSC5q1pdZyHPmLmmYBAvMf0ZNMTmdQB8zbKB54hdUzCIl7D9DBp4/​AZkcXjajXdPwSBewvIzaHQ7gK9c0yae3yvjmoJBooTlZ9DghoptSgzcOaZgEC9h+xm0Jv9myQ7AY9q+KRgxSTP5GTR+JQD47Qkzc0/​BKJM0k59Ba/​RvluwBPEzSWLQH8DBJY9EuwIOmAvA7FYDfqQD8TgXgdyoAv1MB+J0KwO9UAH6nAvA7FYDfqf4fzp8IzGFDAQYAAAAASUVORK5CYII="​ alt="​plot of chunk unnamed-chunk-4"/>​ </p>
 +
 +<p>To plot only one variable, one has to supply it via the zcol parameter.</​p>​
 +
 +<​pre><​code class="​r">​spplot(lidar,​ zcol = &​quot;​Z&​quot;​)
 +</​code></​pre>​
 +
 +<​p><​img 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alt="​plot of chunk unnamed-chunk-5"/>​ </p>
 +
 +<p>As always, the plots are highly configurable. For the example, we define a 
 +color pallet first. The next lines define the labels of the map border (using
 +UTM coordinates).</​p>​
 +
 +<​pre><​code class="​r">​clrs &lt;- colorRampPalette(c(rev(brewer.pal(11,​ &​quot;​Spectral&​quot;​)),​ &​quot;​black&​quot;​))
 +
 +yat = seq(5645000,​ 5647000, 500)
 +ylabs = paste(yat, &​quot;​N&​quot;,​ sep = &​quot;&​quot;​)
 +xat = seq(472000, 484000, 500)
 +xlabs = ifelse(xat &lt; 0, paste(xat, &​quot;​E&​quot;,​ sep = &​quot;&​quot;​),​ ifelse(xat &gt; 0, paste(xat, ​
 +    &​quot;​E&​quot;,​ sep = &​quot;&​quot;​),​ paste(xat, &​quot;&​quot;,​ sep = &​quot;&​quot;​)))
 +spplot(lidar,​ zcol = &​quot;​Z&​quot;,​ col.regions = clrs(1000), scales = list(x = list(at = xat, 
 +    labels = xlabs), y = list(at = yat, labels = ylabs)), colorkey = TRUE, key.space = list(x = 0.1, 
 +    y = 0.1, corner = c(0, 0)), main = &​quot;​LiDAR dataset, ground returns&​quot;​)
 +</​code></​pre>​
 +
 +<​p><​img 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alt="​plot of chunk unnamed-chunk-6"/>​ </p>
 +
 +<​h1>​Visualization of raster data using raster</​h1>​
 +
 +<​p>​From the LiDAR data above we have also derived a raster data set (externally)
 +which gives us the intensity of the LiDAR returns.</​p>​
 +
 +<​p>​Preprocessing is limited to reading the data since it comes with spatial ​
 +reference information in its meta data.
 +data follows the previous examples.</​p>​
 +
 +<​pre><​code class="​r">​intensity &lt;- raster(&​quot;​intensity_1rt1.tif&​quot;,​ native = T)
 +projection(lidar)
 +</​code></​pre>​
 +
 +<​pre><​code>##​ [1] &​quot;​+proj=utm +zone=32 +ellps=GRS80 +units=m&​quot;​
 +</​code></​pre>​
 +
 +<​p>​The raster package supplies both the plot and spplot function. For a simple plot,
 +the former is all you need (here a user-defined color palette is added).</​p>​
 +
 +<​pre><​code class="​r">​plot(intensity)
 +</​code></​pre>​
 +
 +<​p><​img 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wfqd0MH6ndDB+p3Qwfqd0MH6ndDB+p3Qwfqd0MH6ndDB+p3Qwfqd0MH6ndDB+p/​T/​Kwjqa7YHK5kAAAAASUVORK5CYII="​ alt="​plot of chunk unnamed-chunk-8"/>​ </p>
 +
 +<​p>​Again,​ more sophisticated plots are possible (with spplot). For the map borders,
 +we use the information from above again. Parameter at defines the range of the
 +data values which are stretchted to the grey values.</​p>​
 +
 +<​pre><​code class="​r">​intensity.plot &lt;- spplot(intensity,​ maxpixels = 4e+05, main = &​quot;​LiDAR intensity&​quot;, ​
 +    col.regions = colorRampPalette(c(&​quot;​black&​quot;,​ &​quot;​white&​quot;​)),​ at = seq(18, 300), 
 +    panel = function(...) {
 +        grid.rect(gp = gpar(col = NA, fill = &​quot;​grey50&​quot;​))
 +        panel.levelplot(...)
 +    }, scales = list(x = list(at = xat, labels = xlabs), y = list(at = yat, 
 +        labels = ylabs)), colorkey = list(space = &​quot;​top&​quot;,​ width = 1, height = 0.75))
 +print(intensity.plot)
 +</​code></​pre>​
 +
 +<​p><​img 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85zvfuXz58srKSnJyskAgmJ+fh8ev0+nETDIYDI6Pj2dlZWm1WpgI+f3+wsJCi8WyuLiYkpICJb1Go6mpqbl06ZJOpysvL0dhXb9+vclkKigomJ+fj42N1el0CoUCu5l79+7BzWJ6ehpjZIlEMjAwIJVKf/​jDH4KnaDKZKisrEeXhcDhgJeT1euEWCZfgV199tbi4uLe3F2sSfJhpNNpDDz3U1dWFb/​bGjRs1NTWtra0IUMX81u/​3M5lMtAJ8Pt9gMGi1WrFYjIsNQEdTUxNAc4/​Hg82T2+1GUiP2XlgzsDADigGlHdcSll7Mh/​A4oPPAF7lUAoFgbm5OJBIZDIaKioqOjg4ej4dfs1qtXC4X5prBYFAikcDYEtgdg8EIef13d3eHh4e/​9tprV69ehVEaTJKdTid85drb27EhIIS0t7e3tLQwGIympiYwR0FwampqUigUra2t5eXlhJAHhf7B8S8c9fX1EokEDIexsTGdToerbXBwEFUyNjaWSqVGRESsXbs2OzsbZXp1dXVqampkZCQ3N3diYiIpKYkQwuFwFhYW0OZQKBTY2KJ8p6en9/​T0wOeyqKiIw+FIJBI4sMOTLykpCRAEQqBA2BeLxUajET74uBV9Ph+2wBgIgw1tNptzcnJSUlIw4HW5XGB/​x8XFwasS8SNIVTWZTLCC0Wq1MEtISEhAQlt5eTlMDbErB9hiMBgQYgUzZKhwBQIBWkIwH3C3ozgiCGXz5s319fVw2XQ4HLjb/​X4/​zJmTkpIWFhZmZ2fb2tqkUikMy6anp71e7+LiIqYger0+Ly8PsoOhoSEsRcDf+Xx+IBCQyWQZGRkWi0Uul4O9B8TM5/​Nt27atu7t7y5YtU1NTBoMBBCQEXqtUKvidjY+PCwQCjDRKS0vtdvv09DSdTi8oKBgYGEAR9Pl8O3bsAJH81VdfRVyw3W6nUCj37t3DJ2UwGH19fSdPnmxtbT1x4kQwGGxubrbZbJBBwTizqKiIENLV1YX8d5y68PDwoaEhBoMhl8uRW3L79m0Abt///​vfXr19vs9kcDseRI0daWlpgX2o2m8G/​PHXqVHt7O4RRCOMGFIaNFCHEZDKdPn26o6OjrKxMrVZTqVRcbB6PRygUcjgcSBDgRI9rCftOzM8RmeJ0Omtqaq5duwabitra2vPnz9NoNKfTCQATvQ7EcSDX83g8bFAQ2TY/​Pw/​sBUYIHR0d2HlgzLt+/​Xp4WcMODxsCrEkKhSJkLEqlUru7u5ubm4PBINbm1tbWBx39g+NfO+rr68VicXJyMrpvZL3C3jY1NRVEF7Q88/​PzfX19UVFRU1NTo6OjGo0mNTV1bGwsISHBarU6HI6lpSVYUYJqgrkZJmOjo6NpaWkulws5ggsLC+hwwTPD8HZ6eloqlYLkgJsf4CaVShUIBLBqQVmMiYlJTk42GAxerzcxMVEmk8HuHEA/​YjpcLpdWq83KykJfDNjBZrMh6RsQPKrw+vXrwaknhMDD1mQyUSgUxHAnJSXBogRuJ1wud3FxMTk5OSYmJiEhob+/​v7Oz84knnvjVr34FTRN2IVqtlsvlWiyWtLS0srIyFKPY2NhAIMDhcJDvMTs7GwwGMaRVq9UbN26cmppyu93Hjx/​Pz8/​v7u622WwweQ4Gg+np6SqVSi6XBwIBg8Gwc+dO+Kr7fL7Z2VkUGgBiERERvb29gUCgo6PDYDAkJycDeOnu7oY6Fzm64eHhCwsLO3bs0Ol0XV1dCAqG0HRxcdFqtT733HPDw8Pd3d16vd7hcHR3d5tMJpfLxeFwrFYr3I+xHnC53J6eHhaLtWHDht/​+9rcQCWMIiWzFwsJC2FJu3rxZqVRWV1d3d3efOHFidHQU8wx817CcLCws/​Pzzz2FTcerUKYzlKRQKMCJw0m/​evMlkMpETi2FmR0cH4rnB+ySETExMrKys2Gw2IHgY2+Kaxx4LkNTmzZuhdFUoFMPDw4CDYmJicNpbW1vRaNPp9OLi4q6uLpC+4IGBNd7hcEilUpheYIOFRe7y5ctbt25dWFiAjzEhBMwrQgiLxbLZbAaDAcPt48ePw93+zp076GNaW1uxWlRVVTU3NxNC8HNra2tdXd2DQv/​g+JcPFPrR0dGZmRncurATEIvFDocDsYIAoOPi4qA7BZsbDmhjY2MRERFFRUUikWh0dHTz5s1MJlMkEkml0u7ubrlcDgozjHaxryeEoMnCDh1XPDg2wCjT09OXlpZwg4EsCN9jHo8Hk0itVhsZGTk0NARSEJvNBtUd1sT5+fmLi4scDic/​P1+v10MfC2R8ZWUlLS1NLpebTCYOh4O8Q7iZz83NFRUVKZVKvC5i57hc7sTERGxsbGNjo0wmS0tLA2Ef6huDwZCSkiIQCD744IOkpKSysrLJyUkWi3Xw4MHPPvsMzTUIoz/​4wQ+amppgCvRv//​Zv//​mf/​2k0GiHkmZ+fDwaDhw8fDgQCIyMjCoVibGzsV7/​6lUgkgimNyWTCzgMmP+Xl5QgeCi0SERERqNGJiYnwObBarYSQxMTElZUVZLWDV6rRaOD4CLYMjUabmJg4fPiwUChE1hIhBD2v0+ksKyu7fv16yLAsPDwcAPStW7dqa2tzc3Pb29uBUWAZk8vlw8PDWq2WxWKtX78efkQ2m02pVLa0tGDQDZZkc3Ozz+erqKi4du0aUHUmk3n48OHJyUmr1arX6zGchCkQzPL8fr9IJIqMjFxcXAyFPXV0dIAi2dHRgZY5IiLCbrdDLoDFjEqlIhUkGAyisuOyB7hEpVKRfsVgMDo7OzFpB/​REoVAghYXX9LFjx0DUoVKpdXV1t27dwugFJFomkwlAprq6GuOKycnJqKiomZkZdBXHjx/​v6OiAYjYsLAwDBjwdwxJk0lZVVXV1dfn9fgS5nDx5EoYQfD7fbDZj1WlubqZQKOXl5Q8K/​YPjXzjq6+uTkpJgKRwXF6fX6wkhSGjS6XQ+nw8VAeF5lC+illNTUw0GA2DQzZs39/​f3ezyePXv2JCYmfvzxx4jMRmoVfp/​NZms0mqioqNTUVLFYDCt5SCWpVOq6detAr46NjXU4HElJSQBtYD4O2Qu6sLGxMQDoTqczKioqMzOTRqPt2bPHYDBMTU1pNJqSkpKFhYWUlJS4uDhkB8bFxSFjlkajoSdFPjiLxXI4HLixQbIEzoD5J6Jfm5qaMjIyKBRKcXExg8FAUYCDGOx39Ho9UpzKysoGBwefeOKJiYmJW7duIVF9//​794eHhNputsbERZYXP51+7dg2deGpq6tDQEHZLOTk5fX19QMPv3LlDvsjaXbNmzfj4OLY1OTk5zz///​Lvvvuvz+TDLBY6MmgULUth1paWlvfDCC/​39/​aG69vLLL9+4cSMQCJw4caKtrQ0vCrhjYGAAAa0CgSAtLW39+vUY6paWlnZ2dlZXVwM3RzIwylBnZ+ft27f5fL5IJMJa++ijj7a2tqanp/​N4PIQ7IshXrVbDPSkhIUGr1W7YsOHZZ5+dnJyUy+WXLl0SiUSPPPLI5ORkdHT0n//​85w0bNuDbwQgHYbMWiwUyadCx2Gy2w+FISEhYXl7+3ve+NzMzgxOLzR98LjEphbIMl00wGERjgd4fXINgMIgrEHhgVVUVkiDBcjl27NiNGzeAX/​l8PgQrgh7a0tJCCAHSQqVST548+dlnnwH2aW9vx/​Q4NjZ2ZmYGb4AQ0tPT43K5sDUJCwuDkwFMF+B2gPDbiooK/​IC1DW1+Y2MjXhfPRXoX5r1fp9r4oNB/​uQeGsfn5+QMDAyF3XyjIodh2uVylpaU2m62/​v99qtYLCARIbFoD09HT4TcpkMlDWVldXJycnN27cCFGiw+GAwUtOTs7IyIjBYEDlpVAoU1NTYrG4r68P2acYZ6lUqpWVleXl5bS0NAi1kFMKuqTNZktNTfX5fBkZGUqlksFg4I6i0Whr167V6XTR0dFLS0sDAwN2u724uPju3bvr1q0jhIQYmSiLGo0Gb2NoaGjbtm2ABQDcJyYmImuwqKiotLTU4/​EMDw9Dy67T6bA4wcoR+VAI3vrTn/​7U0dGBUS26dXyusrIygPVer7ekpGRxcZHL5WLKmpGRoVKpCCE6nQ5cF+AMcXFxIdL9iRMnIDSz2Wy///​3vCSGwBY2NjcXuR6vVEkKSk5Oxj3E6nVjwrly5kpKSAmy6paUFxaWlpSUqKgpjACxXkAVUVVW1tLRoNBpMWV555ZWLFy+eOnWqvr7e7/​eDQY8fMDmAfQWTyVxYWECGuFgshjNzIBAYHR1FEw20AWcsNja2r6+vv78ffsg+n+/​o0aPvvPMOHCLDwsJUKtWRI0eGhoYwsXA4HLAMCgQC3/​3ud9vb2wkhYC5iq4R0PSzVIAVhiIqpD/​p0iLAwiqDT6S0tLYFAIC8vD3m5hBAQHykUSm9vL/​g80FtMT097PJ7Tp093d3eHaGOo2iwWC4asGFeUlZXpdDqj0RiihEJCER4e7vf7wfgEzoMJMLp4r9dbV1fX3NwMkk8IkGlqasKNyWQyq6ur6+vr8cWhS3jxxRexEoA49HWqjQ8K/​Zd7ALqJjY2dmpoCvTc9PV0ul2NsmJCQwOVyFxYWlpeXoSEKDw+Xy+XgTTIYjPj4+Fu3bsFsJBAIDA0NTUxMQBK1sLAgEokAtvT29m7bts1isWg0GpgHYDS6fv36iYkJgO9oG5HP53Q6uVzu/​Py8y+WCQYJYLF5YWEA6KMQ44IHg/​tHr9TExMRwOx+v16vV6sVhcVFQ0Pz9vtVr37t0bERFhMpkwfOvr67NYLBkZGfDjhM+BQCBQqVROpxOmN0jQhtzU7XbPz88zmcyEhAQajQYDHCwYABCoVOrg4ODExASCs4G6RkdHf/​vb375+/​bpYLJ6cnOTxeFFRUSwWy2w2w1NFIBB4PJ7ExESDwcBisZaXl0tLS6HjR2QSGCCw0m1paRkdHYUGLT09/​cCBA62trcg1ValUIpEIsD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hCA4OTkhIgBvE9PS0RCIZHBx84oknzp0719vbi9+yYsWKqampxsbGtrY2FAaHh4dzcnKioqIWL178xRdfYFTA/​fff39DQsG3btnPnzrlcru7u7rS0tDfeeCMzM1MikQwMDOzfv//​w4cOzs7OFhYWffvopSj2YBzI1NQVjeuQEHh4eDQ0NixcvtlgsbDY7JiZGqVSiqHX58mXYyZ04cQL5uMViaW1thXsP3kOMXYNQAMUisVjc1NTkdm17/​vnnly1b1tDQwGQycQjw8PDYvXv3O++88+yzz7a3t+OQDcrmQoqNVKC/​t0DrXyaTQWekVqtHRkYmJycxD3PJkiWIHWw2G0FWpVJBH7to0aIPP/​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​Px8tnMLCwv7+/​qqqqsbGRhTcbDYbejANDQ2enp5DQ0P4TEIaptFoFlJspAL9vQVYN01NTXa7XafTuVyuJUuW9PX1Wa1WeINgtpFKpUpISDAYDDExMTdv3oyOjkaJE54kYAHia4Die1NTU2pqKkKMRqOBCaVUKsUgkZUrV3p4eKAZq9fr4Qyu1+sZDEZ4eLjJZJLL5QkJCVqtNjo62mw2g8wD65Lx8XHMmZqeng4KClIoFP39/​bOzsyhJe3l5rV27tqWlJSsrCyk/​yCFhYWHDw8Ng+4GyCSMtkHwwSAStyOnpafd8Etgmt7e3E0IwDrSqqgo6l8ceeww9N/​yD+CYrlUpI4WdmZoKCgjDXsK2tTafT2Ww2+MRptVpUjW0229WrV2Gs//​jjj2s0GtSXhULh0NCQTCaDdz+sj2He6XA4urq6UMsCcwMtPhqNhqGJcGCempqanJwsLS09d+4ci8VC5aGkpKS2thZltOHh4aGhITQP0cZ48cUXW1paoqOjRSLRJ598EhoaarFYcnNzR0dHNRpNamqqm9Xq6+sbFRVls9lKSkri4uI6OjpwYigpKdFqtXw+32q1xsXFtba28ng8gUBAp9OXLFmCUDU9PR0VFYUTYVRUlEqlkkql69atO378ONrRsLjIz89H599qtcL9/​yc/​+Ymfn9+qVatSU1P7+/​sxalGhUCgUCph9wr3j+PHjvr6+AwMDfX199fX1XC73pz/​9qVQqLSsr6+npAZcJHqjoAKHhv3Hjxra2Nm9v74aGBswpczqd8MDx8vIqLS09deoUKntQacFsEvq46elpoVAIdSsU1JBzs1gsiGlv3ryZmJiYkZGRmJh44sQJUGNB/​gFdFTMCWSyWVCp1m49arVY2mw3lFLruNBpNq9XGxcVZrdaxsTG1Wu1wOBZSbKRYN/​cWKIsHBgZ6eHiMj4+z2WywfVEihPFkZ2fn6tWrobMHTQ0O4NB0QDe0evXqjo4Oh8MhFAoDAgLGx8ddLhfEnBKJ5MyZM4mJiWAEOp1OFExSUlI0Go1YLIaLAGoOOp0uJSWlsbExJiYGdq9BQUHoMZrNZm9v7+HhYXQg3c4q2ACgSvX09ExLSyOEwJeRRqMJBALQ7SFeNRqNsJjHa0NX02azDQ4Ojo2NhYWFMZlM2O1qtVrQq5ubmxHT4dAAT4iEhARIItva2sRiMQy2MKvabreDLoL3lsfjMZlMKLzy8/​M9PDwuXLgArp6b+DgwMDA4OBgQEPDMM8+8/​PLLsFbGN/​z5558/​fPhwUVFRb29vSkrKF198gYCIGbwCgSAmJubGjRvYZSUSSXd3N+hJO3bseOONN5AS4gABFf7evXtffvllyH/​A6kOQdRPAIYOCnAq2XJmZmbOzsyqVCn11+CJ4eHjs27cPs/​HAw0lJSbl+/​TqcBhgMRmlpKSEE7jToS0dGRj755JPd3d1arRbuaTqdDmcmSI49PT1v3LixdOlSHIxUKlVGRgamvuh0uq6uLhxT6HQ6DOWTk5OZTOalS5e0Wi1qHTg3qFSq+Ph4pVIJ4QXMCZhMZkFBwUsvvcTj8WB08fjjj/​f19dXU1ISEhID729nZmZycLJVK4+PjYY4vk8mQx7jHkoSEhOAE43Q6WSyWxWKh0+loOEEc5+npCXc2DoeDtm1ERITJZMLhGLK4pUuXmkwmf39/​i8UyMDCwZMmSqakpjE0uLi4mhPzxj3+E4Qe8RTkcTm9v78qVK1tbWz09PWEntZBiI5XR31tUVFQIBILQ0FA4DYSEhCgUivj4+NDQ0JMnTyIRDg8PHxwcfOCBB/​r6+mBYGBQU5Ovr6z47WyyWrq6u1NTUiYkJNxewp6dnfHzcYDCkp6fDrqShoWHRokWgKqpUqpCQEMgXkVyj0We1WkHHFIlEMJeH1QF2gsjISOTakImiX4eEF/​YM+DcjIyMJIWq1WigUOhwOu92OsXYOhwPdTvQJUJQQiUQMBqO/​vx86ALlczuPxUJ2g0+kXLlxAxooIzuPxHnrooZ6eHuhrpFIpKBMqlWrlypUoMkRHR6Oh5+npGR8fj7MRvpOrV6/​+9NNP4VrD5/​PXrl27fPlyEEImJiYsFgvG9VksloiICLPZ7O/​vDxfDn/​3sZ1988YVareZyuXl5eYmJiVNTUyKRaHBwEJIFED/​QQEb639DQAKqPzWZLSEgApYdOp4NgQwiZmZnBmDofH5+wsDCDwcDhcNhsNiGETqcXFxebTCY/​Pz8Oh3Pt2jXkuSjywKvA6XTm5eVVV1eHhoZWVVWtWrWqpaUFcz/​QMz916hRGhKNTgvm0TU1N6enpLS0t4JCw2ezh4eHx8XGr1bp169ampqbp6emNGzdWV1f7+vquX7++sbGREDI9PQ0/​4aysLIvFolKpbt68+dRTT928eVMmk01NTYHpZDKZyJztPs4u2dnZoOrqdDqNRoPxsOj6gMWrUqnodDruOOw6lixZYrVai4qK2traZmdnN23adPXqVSiioSxDU8Fms+3bt6+9vR21HegVNm/​e3NbWhh4Jh8PB8RcNJxaLBTryI488QqPRent7TSbTyMjIo48+2t/​fDzJYcnJyf39/​bW1ta2srvgvbtm1rb2/​n8/​mwxvTx8YmPj4+Njc3MzFxgzdh5poyt2kqbQ/​ZhBSGEKA5nf3lha9Wtj1Qczv7yEd/​1rLkr7ofcxhXF4exbfkfV1m/​8wn8KVLTXrFkDAQ4SvZmZmccee2x8fNzX11cmkwkEgn/​84x+JiYlarRZDiG7cuNHU1LR+/​frPP/​9cp9PBMNJsNkul0ujoaNBREJRlMpmvry+8aIaGhhQKBfjss7OzWVlZGDACOrxGozGbzXDXOX/​+PL6l+CIRQpKSkmAlRgjRarX47qG+D0qoRCKRSCSbNm0ihEAHi683tLLgKWKc4cTEhEKhQBcapDd88+FXBf8Gm80GWSnYdXa7HfnXsWPHgoODe3t7EUZpNNro6CgszMCdDwsLy87OjoiI4PF4N27cgM4Iei40fkE4MZvNZ8+e/​fjjj0NDQzdv3pyampqVlbVr1y6DwQCJr9PpxGxeBoNRWVkpkUjWrVsnFArHxsZOnjwplUp7enqYTGZiYmJmZiaiidVqjYqKgmH99PT07Ows9jyZTIb2tdVq3bNnD+ryQUFBhw4dwgdArVbjhWFaSFRU1NGjR2FVvXHjRhDqi4uLIXt+7bXXCCE0Gg3ek3D7amlp8fb2zszMDA0NRZ0BTBu4ST/​wwAPYpUpKSt57772UlJStW7ey2ez8/​PyHHnoI28DHH38cFxcXERGBLY3BYLS3t8NUDkTYkpIStVrt5+eXl5e3cePGt95669KlS/​jUicVieC0g2y0qKlq0aFFBQUF3d/​fvf//​7oKAgPp+PPsejjz6KnRtdfQxE8/​T05HK5GRkZMBA2GAx/​+tOfMPLpk08+2b17t9ls5nK5hJCXXnrJPT/​99ddfR1ICVReHw/​nLX/​6CQWlBQUHh4eG9vb04FhNC4uLiBAJBSkrKxx9/​PDIy8vDDD0skEkLI+++/​Dy9Yl8t1//​33i0Siffv2wT7B5XK9+eabMzMzOp2ur68vJiZmw4YNLS0tFy9evIOQNE8wvwK9oleadUgOKUN9sYSQqq2x3WX4eyVZf2sYjn2x4XuetV56SO5yuSqTXvy/​wwpye1cIIVnSg7cZ3W+Br6/​v4OBgb28vl8tVq9UcDqelpcXHx+fs2bMw/​YDWNCwsbHBwEPkah8MB5/​3VV1+FSjAtLW1kZASR7urVq+B9p6enQ5sKelxYWBj6lriCxHNkZESv10MDBV6Hy+VKSEhISUmBPSRSMxRJDQYDIQQio+TkZAzDs1gs4OFFRERUV1eDTj46Okqn0xkMBovFMpvNk5OToLKpVCq4yMKWBHl6bW0tAjpO+ggERqOxvb0d5Xs0KkdHR5Hmj4+Px8XF7d+/​PzAwEBIt9DMEAoGnp6dSqbx+/​Xp4eHhRURG0lCh0pKWltbW1hYSEgODv5eWF/​eDcuXNdXV3Xr19XKBSXL1/​Ozc318fHJz8/​Py8vLzs5+6qmn/​P39W1tbQ0JC4GBcU1ODIgwhxOFwmM3m2tpavV6P447bnA507IGBAZRuUJtGws5gMJYuXYpyx+LFiwMDA6enp5OSkpKSkkJDQzEwb3R0FAbUFRUVYPu99NJLHh4eEDEQQg4cOCASibhcbmlpqVtDd/​HiRais0bpnMpn+/​v6bNm36+9//​7nK5UlJS/​va3vzGZzNOnTx89enR2dvYPf/​gDuqn4RM3MzCBJT0lJGRkZeeSRR1BYCw8PX7169XvvvYeOyMTEREdHR1ZWVmBgYHh4eGxsrFKpjIuL27JlS0lJiVwun5iYkMlkIHQ+/​fTToOqvWLHCZrN98MEHTCaztLQUdTzM4FWr1d7e3j09PceOHUPnmc1mDw4O+vr6qtVqVLrgKc9gMNhstr+/​P4Yr4MMM+rzRaNTr9TqdbmZmxmg09vT0rFixAm7J0dHRBQUFMpns6tWrfD7fYDB0d3dzOJz169dnZGS4B/​8SQnQ6XUVFxTvvvMNisaAzmJ6eRmMATvf4zr766qt3Hp3+u+GaT6jckpWVhde9pdLlcskPZW3ZsoXccsWFq1mH5JVb5qL7dzzrqx+RLZW3d8UlP5S1pVJ+KAu/​Z+7q1/​Cj3EEKFCj8Z3Bvw9u9xLzK6KuOH2lIQgIvTzy4tYrIuxuOkCLXLVe+TPLriyX//​FmEkKQ4/​Dw28csd4LauEEIkxWW3nhy+gW+/​v995cQHjf2q91GIXMP7Z13mewuvHfgF3gsJ33W+1JC5J2qvYmZi1Ja7w1iuK3oNHSMMR2hFCCCFHsom8vvibzyKESHsVpFBCiLy7gcQRcptXvnwRlcdpW6sq7/​1yKVCgQOHfgvmV0W91t0oVvdKkOIkkLunI8aqvXSmu/​3LzrdySdUheXyz5zmc1/​P2UghBSdfxIVmIsIbdz5SsUvltJ1q8/​8h9bNgUKFCj8a/​iPHof+ZVTOFeTd3VX5oaxvXHE/​1H3l28+au/​LVk374irs8P/​fDb9fov4159w7/​i/​ifWi+12AWMBbZeSjB1bwEjsB/​7Vfzn8D+1XmqxCxgLbL3zqnRDgQIFChTuHAtq16JAgQIFCt8GldFToECBwgIHFegpUKBAYYGDCvQUKFCgsMBBBXoKFChQWOCgAj0FChQoLHBQgZ4CBQoUFjioQH9b+C53+zvxsr9dv/​uv4c4dkf9tuNv13onj/​3/​Ner9/​sXOXvzaKYP7e3Ltd7Ly8s+QH1nvLC527Nq9v7vfhR1TlzhtUbiFuJ4TKLV/​+6d/​+h1uckX9k3Ol6v/​3S59F6v3+xLrcVxpzhxby+uXe62Hl9Z1238Um+5a5uqZznN/​d7QWX0PwTF4eyDiXK3Xc6tpsVJcRKMNXl8g4QQUli0paFbfrdX/​ltw5+slRN5NyN9jv0pm5s16f2ixhCgO9xbd4qg0n2/​unS92Ht9ZchvrLXzX9W7hrT+cxzf3h0AF+u/​Ht9ztJcX1Zd2xNBrtYKJ87lNyd+723+F33/​Bi7I98/​rur9d6t4/​+Pvd7bWaykuLjwG0+bnzf3rhY7X+8suc1P8q2PxbX5eXN/​GFSg/​z4oDh88Qo6sp9Fo6480vBibfVihOJxNO17kcrlcR8n/​zd1ReNzDuZ78C1e+7sD57jcDzL3HXa638F33q/​2a4/​+tj/​nvW+9tLvbbmI839y4XOz/​vLLmT9VZtpdGOF7lf43y8ubcDKtB/​H77D3Z6QL93pJXFJ5Mv/​34W7/​ff53f94uLv13p3j/​4+O21nsdzxrft7cu1vsPL2z5HbXOxf83/​1qeNF8vLm3hTuu6v9v4it3+68qmO6O1d2423+n3/​3Xb82P2eC5y/​XeieP/​f896v3exc9e/​Potg/​t7cu1zs/​Lyzru9br3tpt77I+X1z/​zko90oKFChQWOCgSjcUKFCgsMBBBXoKFChQWOCgAj0FChQoLHBQgZ4CBQoUFjioQE+BAgUKCxxUoKdAgQKFBQ4q0FOgQIHCAgcV6ClQoEBhgYMK9BQoUKCwwEEFegoUKFBY4KACPQUKFCgscFCBngIFChQWOKhAT4ECBQoLHFSgp0CBAoUFDirQU6BAgcICBxXoKVCgQGGBgwr0FChQoLDAQQV6ChQoUFjgoAI9BQoUKCxwUIGeAgUKFBY4qEBPgQIFCgscVKCnQIEChQWO/​wcNWhZUX3wF0QAAAABJRU5ErkJggg=="​ alt="​plot of chunk unnamed-chunk-9"/>​ </p>
 +
 +<​h1>​Visualization of polygon vector data using sp</​h1>​
 +
 +<​p>​The last example shows the visualization of a polygon vector file in ESRI&#​39;​s
 +shape format.</​p>​
 +
 +<​p>​Since this shape file comes with spatial reference, preprocessing is again
 +restricted to reading the data.</​p>​
 +
 +<​pre><​code class="​r">​polygon &lt;- readOGR(&​quot;​polygon.shp&​quot;,​ layer = &​quot;​polygon&​quot;​)
 +</​code></​pre>​
 +
 +<​pre><​code>##​ OGR data source with driver: ESRI Shapefile ​
 +## Source: &​quot;​polygon.shp&​quot;,​ layer: &​quot;​polygon&​quot;​
 +## with 16 features and 3 fields
 +## Feature type: wkbPolygon with 2 dimensions
 +</​code></​pre>​
 +
 +<​p>​For plooting we use spplot again.</​p>​
 +
 +<​pre><​code class="​r">​polygon.plot &lt;- spplot(polygon[,​ 1], col.regions = FALSE, col = &​quot;​red&​quot;​)
 +print(polygon.plot)
 +</​code></​pre>​
 +
 +<​p><​img src="​data:​image/​png;​base64,​iVBORw0KGgoAAAANSUhEUgAAAfgAAAGwCAMAAABy/​YdMAAAARVBMVEUAAAAAADoAOpAAZrY6OpA6kNtmAGZmOpBmtv+QOgCQOmaQ2/​+2ZgC2///​bkDrbtmbb////​AAD/​tmb/​25D//​7b//​9v////​HAKStAAAACXBIWXMAAAsSAAALEgHS3X78AAAH2UlEQVR4nO3c604bWQAE4QHCJWAc49h+/​0ddT2B3WW3IuHOU2OmqFgojdDSF9DHY/​Ml0cMhN5/​4G3HkmPHTCQyc8dMJDJzx0wkMnPHTCQyc8dMJDJzx0wkMnPHTCQyc8dIvwk7ucfSDya+B/​5q7ZXkyctundv//​78k/​da+TA+DpUhI/​XoSJ8vA4V4eN1qAgfr0NF+HgdKsLH61ARPl6HivDxOlSEj9ehIny8DhXh43WoCB+vQ0X4eB0qwsfrUBE+XoeK8PE6VISP16EifLwOFeHjdagIH69DRfh4HSrCx+tQET5eh4rw8TpUhI/​XoSJ8vA4V4eN1qAgfr0NF+HgdKsLH61ARPl6HivDxOlSEj9ehIny8DhXh43WoCB+vQ+UC4FfT1dPb5fr2pHuNHBhfh8r54bfXm+PH6+UkfFHiI/​i3/​+vu+JTv7p7nr3z99EX4osTCE79+OOwfZ/​gj/​1b4osQS/​N9P/​Hr+FbAgL/​wflDj9Nf7gE9+UOOld/​e5+the+KeHf8dCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NCE8NDE74V/​cZcyn3hoQnhoQnhoQnhoQnhoQnhoQnhoQnhoQnhoQnhoQnhoQnhoQnhoQnhoQnhoQnhoQnhoQnhoYgl+NV09zZ/​3n6fpenPKvUYOjK9D5fzw2+vN9pv39ub4Q/​Bwyr1GDoyvQ+WM8POOn9e3h93d89sX18L3JBae+KP1/​vENfn7oT7jXyIHxdahcAPy/​T/​xqyV34Pylx6mv8/​vPC7/​nv3SU+ML4OlfPDv76r391vVvOL/​u0p9xo5ML4OlQuAz+81cmB8HSrCx+tQET5eh4rw8TpUhI/​XoSJ8vA4V4eN1qAgfr0NF+HgdKsLH61ARPl6HivDxOlSEj9ehIny8DhXh43WoCB+vQ0X4eB0qwsfrUBE+XoeK8PE6VISP16EifLwOFeHjdagIH69DRfh4HSrCx+tQET5eh4rw8TpUhI/​XoSJ8vA4V4eN1qAgfr0NF+HgdKsLH61ARPl6HivDxOlSEj9ehIny8DhXh43WoCB+vQ0X4eB0qwsfrUBE+XoeK8PE6VISP16EifLwOFeHjdagIH69DRfh4HSrCx+tQET5eh4rw8TpUhI/​XoSJ8vA4V4eN1qAgfr0NF+HgdKsLH61ARPl6HivDxOlSEj9ehIny8DhXh43WoCB+vQ0X4eB0qwsfrUBE+XoeK8PE6VISP16EifLwOFeHjdagIH69D5QLgV9PV038vlu41cmB8HSrnh99eb44f7y8W7/​WjAy/​uUvYR/​Lzj5/​XtYXf3/​P5iCH7pwPg6HsfzP/​Hrh8P+8fn9xeK9Rg6Mr0PlAuB94ksTv/​c1Pv724nWonB/​+9c387n7ju/​qyhH/​HQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxPCQxMnwK+mq6f58/​7zNF1vFu81cmB8HSoXAb+93my/​eW9vjj8ED4v3Gjkwvg6VM8LPe71c3x52d89vX18LX5JYfuKP1vvHN/​j5oV+618iB8XWonB9+Nd28e+JXP3QX/​k9KBK/​x+88/​+j3/​vbvEB8bXoXIR8K/​v6nf3m9X8un+7eK+RA+PrULkM+PBeIwfG16EifLwOFeHjdagIH69DRfh4HSrCx+tQET5eh4rw8TpUhI/​XoSJ8vA4V4eN1qAgfr0NF+HgdKnXwL+5S5hMPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTQgPTZwAv5qunt4u17fL9xo5ML4OlYuA315vjh+vl5PwLYmP4Oe9Xh6f8t3d83z19dMX4VsSy0/​8+uGwf5zhj/​xb4VsSC/​Cr6eafJ349/​xb4kbzwf1Aieo0/​+MTXJE59V7+7n+2Fr0n4dzw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw0ITw08Xvh3eXsA5FfAu86Jzx0wkMnPHTCQyc8dMJDJzx0wkMnPHTCQyc8dMJDJzx0wkMnPHTCQyc8dH8BI7MM98sQBzwAAAAASUVORK5CYII="​ alt="​plot of chunk unnamed-chunk-11"/>​ </p>
 +
 +<p>As one can see, the polygon just consists of several grid boxes. Let&#​39;​s
 +overlay it to the map of the intensity data from above.</​p>​
 +
 +<​pre><​code class="​r">​print(intensity.plot + as.layer(polygon.plot))
 +</​code></​pre>​
 +
 +<​p><​img 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qIRF3y5YtMzMzGMbxsbfb7Uql0m63Yw1LCNFqtU888QQh5OTJk263GxB5MBiEsWh0dHRCQoLH4zEajYC8PB5PSUmJVqudnZ01GAz5+flMJtNqtaIrl8vl1wcHWUymTCbT6XS1tbV//​vOfbTYbiunBgwf/​67/​+y2AwsNlsLpeL7HUc4fPz888999wXX3wBWRPqFFyD8EeZTDY2Nkaj0bCHAK8Gn1MMMXv27Hn77beDwSAabSAnhBAMYRwOZ2VlBV08/​hFCCJYrGLn8fv/​mzZtBpzlw4MA777wDAB2BtAD3aDcCtUH7qa+vD4ZCVAqF3JAsAM2jUqmwOsB1/​Pjx3bt3nz59uqenBxUf6028gIqKisHBQaPRmJCQYDabkWaza9eut99+G0MAwljwMnbu3PmnP/​0pEAisW7ducXHRbDbb7fYnn3zy888/​x1cWFxdHR0c3NDTAnwMTA3ZCGCLD4TCfz09KSurp6Vm7dm1fXx/​27RqNBnU/​IkJ0uVy7d+9+5513ADqh+gM5/​LcWi5t53V7G3twLgimQEcH2XVxcHBsbk0qlII3o9Xo4lsCoQCwWq1Qq+H+Njo7KZDK5XB4TEwP2GHzVLRZLVFQUg8GA6gSJdHa7XaFQwPQDMcd4TJHUjLIFfTnM7iFiQhwgxI2oyHBJAz8S7GYEEwJ1xc4WZGelUvnDDz+MjIwgQ8Nmsy0vL/​9qbOyD6GibzTY2NoYAbqPRiO8Fiy4lJcXr9SYlJWVlZXE4HCx+bTbbyMjIli1bdDqdz+d74okn2traEMaCOBEYTHI4HB6PNz4+npyc3NPTA/​MDJpOZkZExPz8fFRWFIsVms5FDC5chgUAwPj4Ohv53330H7AjR1ZhLCCEKhWJsbAy53hhQYNEzMTHh9XpXVlZcLtdLL71ks9mMRmMkO2X30tLBYBDR5D/​88EN5eflTTz0FgdVPP/​2EEAK5XG6322022/​bt2+FH5HQ629vbsevGsYRErYiBjMViQQFtamqC4pfNZiclJSG0a//​+/​UeOHNm/​f39jYyPEpRs3bmxvbw+FQgg0R1Pf1NSUl5eXmZk5Pz//​wAMPzM7OQlYK4G52djY5OVksFn/​33Xco5a2trTabDZ7AgUCgpqamqamJx+Ox2ezz588fPHiwoqnp/​Lp1oN+ARH/​9+nWXy1VRUfHWW281NjY2NjbCrd5qtYZCoZaWlk2bNiGgFSUYsTlxcXHwVtq5c+eFCxdaW1ufe+45aM3kcrnH41m7du3ExARu2pEjR06dOhUbG+twOI4cOfLhhx9iVktPT09LS5PL5Uaj0eFwbNy4UaPRYNGKpBGcImCOEULm5uaYTObvfvc7KCcwxbLZbJwHIpHo7NmzlZWVIyMjPB4vOTnZZDJ5PJ5bqTbe7uhv7kWhUO6///​6oqCi9Xi8QCJKSkhDfYzAYUlJSzGbz7OwszgCBQOBwOBC1ER8fHwqFkPgTFRUVHR2Npl6n00GlyePxTCZTVFQU/​FU0Gs2VK1d8Pl9ZWZnVauXz+SaTKSMjg0KhiEQirDrdbjd8uqGcCgaDcM0EwwG9OZwgXS6X1WpdWFgoLS01m82goACRgCYWKXfnz5+PWJ7hB4XD4ZHR0fy8PACg8GVDU4ktpUAgCAQCsbGxsCPGLWKxWN3d3SUlJUtLSxMTE1QqdW5ubsOGDSjTEIhhz4zMvFOnTsXFxY2NjSELEDbler0+HA5DWozVqEAgGBoaGhkZue+++yYnJ51OJxRVXq83LS0tNze3ublZJpPNzMwgwhAQMDSc0dHRV69epdPpcXFxNBoNTB4Oh8NisUD5B19lano6MyMDv6PRaMRtiYqKCofDHo/​H7Xbfe++9SBRxOp0ikSji+EhuWPvijcCtQBw5VA5isXh5ebmysvK9994DF8Xj8UgkEpjao2cnhNTW1nZ2duImtLS0QGCBNwU2+qD841BH4BcOhm+++QZ9sclkiuiq3n//​fZxqFAoFjjSA2iHI+n1dHY1KBQuTEFJXV4e1LX4doDeJiYkQPcGaAtZmhw8fRtf/​5ptvUqlU3O1IpIFMJrPZbG63OykpaW5uDoxYRIJQqdQtW7acOXMGPF34swYCgd///​vevvvqqSqUymUx5eXnx8fG9vb0I9nE6nfHx8Xl5eevXr4dICkMqvHQgDhcIBG63u7q6+u2338brD4fDWBQnJSV5PJ709HS73Q6+0/​+TonEzrtvL2Jt+wS0A+1idTgdiAOxnDQYDGlU8bWAUoN2O6PsnJiYIISkpKXBzDYVCLpeLEIIWKSkp6erVq3a7fcuWLeFwWKlUwkJAqVR2dHQgaxAHBqBqRHXjk4ZgJjDowX9YXl6em5sDJLJ69Wqn0wn2JMykTCYTgEswQbEDAL4J8xxQwsF0dLvdWq0WrmQsFguR3xQKJTo6msvlXrlyBVGfcBfZuHEjDozVq1cjCeTKlStcLjc6Ohq7QafTmZSUhG40QhvHJA4H4KysrIyMDCyNjUbjt99+C+JNeXm53+9Xq9X4XoVCoVKpZDJZd3e3SCQCmTIrKws3BFKmUCh09epVuVyen5+/​vLxssVhg1h8VFcXn82dnZ+fm5kD3JoRAzgqVg1QqjYuLgzIOv+bZs2e3bdsGwozT6aypqZFKpdgAgxgKzg8Uwrg5lZWVNTU1bre7srLy6NGjDodDq9Xu27ePEAKZNCFEqVRu3bo1ISHho48+On/​+fG9v76VLl6hUak5ODpyuEa5CoVBWrVoVGxuLNEd4/​I6NjV24cCE6OhoprAqF4u9//​zsh5N1334VDBkR8bre7qKjo4MGDqPIgxWNtDmJ+bW0tlLEHDx7k8XhYgczPzwPc37VrF6Rnb7/​9NiHk8OHD9fX1+/​fvB6uSEAKyGSi5r7/​+emxsrEajgcLAZDKxWKxXX3312LFj3333HRyW3nvvPfBhHnvssVdffbWgoABn9vT09OnTp7dt24a5FpbO//​mf/​7lv3z6o7fh8/​vbt2zHlMJnMnJyc4uLiuLi47777DtsRQggcYePj4/​V6PYYJpHrdStftjv7mXhQKZevWreC6RBoxxJa6XC5EPoHtgAQ7DNdggCE3anx83OfzicXi4uJiGDfCuJzP52s0mujo6OHhYWiX7rzzznfeeQcGT3Q6ffXq1Uwmc2lpCea6gNcdDgeFQsGOFKQ3uBpcvnwZiicWi4WwclSriIU9CrHdbkfLr1Kp4EcIqARSKY/​HMzk1pYyNRXAHIQQKAGBE2dnZyCUfHx8vLS212+0qleqLL7544IEHcHqBPB4KhRYXFzdt2sTj8RCfC5UAdpVisfinn35KSEiYmJj41a9+pdPpKBRKUlISlpkIYFlaWsIuFPcWvaHT6ZydnYXnM2wDYCYKLRiTyZyYmIiJiYmNjQUgHhUVpdVqMzIy+vv7hUIhvHmBDsEMWSQSzc3PczkcHHuVlZWEkCNHjgBorq2thdHYwsLC/​8VoxE1DB7pnz5733nvP5XJFkHQ6nS6VSgFb4UTX6XTAGaBT+9nPfoYE8Lfeegt5NaWlpdgtY2nMZDL37dv34YcfWiwWdOsMBkOlUsFlITk5eXp6GrtxJpOJ9MQDBw588sknCJuEsw0SDjgczuTkZEFBwczMjNFkEvD5NBoNRymeVYRfQjDx2muvYW0LBL+mpubtt9/​GuuKtt96CZ9lDDz30r3/​9q6am5k9/​+pPL5SopKYHzhMPhgLHo1atXMTjS6XQ47LvdbiyBsMGKuHMzGAy4/​WA4AM9q+/​btx48fZzAYcXFx6enpzc3Nzz777CeffJKbm4vIFwaDIRQKW1pacBBCUILQ4127dv3P//​wPIcRgMDz55JOfffbZrVQbbxf6m3tRKJTKykpwP3p7ezkcDsyYfD6fTCYzGAxg0YCigLqDz7nb7cas7ff7p6enQcMXiUQUCsVoNAoEAqTOgrzhcDgMBgM+Kp988gmAiNTUVHSaGRkZSUlJKN9Ig4I3mcfjgUsahUI5d+6cxWLBwlYoFBYWFuIkAKAJ8rvb7b527Rq2ozB6RQsfGxsbDocNBgODwdAbDDFyuUgkYjKZDocDIVbw4AQXPiYm5vLlyw8//​PD4+Hhzc/​PmzZuBJoGv4vF45ufn8/​PzLRaL2WxG30elUicmJtRqNQJALl68mJub29vbW1paCuQqsvIFDAKYAmiP3W7H5s1gMIDXJBKJcPBgnQgkNycnp7u7W6VS2e12gLw8Hs9iseDujY2NxcTEALxGIcPOYEGvT0xIgBNcfHw81htCoTAzM7Orq6umpuavf/​3rysoKDBoJIR988AEiAaqrq1ETcSLabLaMjAyscMxmM1IHyA0LGiiTCSHgRDmdzlAo5PP54AMDJS0hRCAQLC0t1dbWfvDBBw6HIyYmBvRwl8uVnZ09MTGxZ8+ew4cP0+n0yspKxOzhEUXUOIJW+Hx+WlpaT09P5GYSQjgczurVq9va29kslkwm02q1hBAmk6lUKjHnRUdHp6amqtXq69eve71eZKyju+dyucgMwB04dOgQbiODwXjyySc//​fRTiEvYbPbOnbXIj4IAACAASURBVDuBpWDZC+dUn88HXlBMTIxGoxEKhYFAwO12g00EiGllZUWtVufl5f3zn//​Mz8+H47RSqZyYmGCz2RijQcJxuVzg+8L/​A3HNRqORy+VmZGQMDQ0B2rJarWw222g03kq18TZ0c9OvhYUFj8cTCAQWFhbGxsZ0Oh2w5oWFBbPZjHAPQggMLHk8HgB6gCFyudzv96elpQHHAHunuLgYKR/​AXvDBw2YyGAw+//​zz6BmnpqZ+97vfORwOwAUwkAIEjJ0Vdo+AZRwOB3ZZS0tL69atQ9gbIcTpdI6NjS0uLl6/​fp3BYCDABHtOsNZQTxUKhVKpxGiP04gQAr0iWjDY2giFQqPRuG3bNqvV2t7eXlZWBpIPqvPCwgL8agYHB0H5gFr10qVLMEam0WjgyUxNTSUmJqI0e73e3t5e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k7O/​Px8YWFhJBKZmJjAImrJkiVUXqlUWlFRgWN+IBDAQhnFitFoZOsIkSY2Ntbr9S5btgwWDSMF7CCn0+lyuSS3IvpwxFWr1XghJCQkcOaZzWaOh76+vrNnz/​JIZ2VlxcbGbt++nUKsVqu1Wu3q1avZYZLxHc0pJPkPu7QDBw5IpdKMjIzu7m74+ywVjEajRCJJT08XQuAzwZIjerBx3AJ8VwNsAAAgAElEQVRWJCUlRSIRSiSLBCIbOTOANfx+/​3333bdo0aITJ06Ew2GMegCI2tvb77vvvk2bNn344YdpaWlmsxknfYjk586dq62tTUxMVKlU3d3dx44dw684MzMT6gu8e+4ocGTWOVVVVV6v99FHH7Xb7UIIt9s9PDxcVVX1wAMPcCQgzRVChMPh48ePs4Wmuean4Z9MY1tXV8f4Em1sZTLZT3/​6UyFEVVWVEAKshgv+jxACqIopLRgMrl27VqlUYv4BYQmZEngLfnPRB4TNMD+N30fkAZ2Gvh7tG07UdP1AYZRy1E+cgsDxmZmZzIsymQwHJ+BBKMg+n6+2tpbDcteuXXV1dSBjjCwqlYrkqTvpusujv71XQ0OD1+vdunXrP//​zP8OIuHr1ak9PT2FhIXEfPE4EGCG5DAQClCdyk5HUm81mJFFwZjCs9/​v9eXl5OCliQmk2mzG6QZwphMDsHhd1iiCtPU8UUC8VJCpGBehYsmQJ2YdpaWlWq5VaXFBQ0NfXp1AofD4fPsNqtbq5uVmpVCYkJKjV6icHB99bvJhWDrIQbRFqw4WFBZ1Od/​PmTZPJlJycrFAoYN/​zarF95zALhUItLS1Go3HZsmXIeumply1b5na7oVq6XK6kpKTi4mIafwiFuNCwMMDxH21OSUnJsmXLeLbxPsNjC3oMPG6Scok/​RIGF4oHgEQTAMKZQDzze03Nm0yY2JcDQQMaFhYWojWJiYr785S+fOHECrotUKl2xYkVbW5vNZhscHOSnud3usbEx/​IiMRuPFixc9Hg/​xgUQEs67k2wE+Jv6wp6eH/​9vc3FxUVPTee++tXr2aczQ2NhY/​nGefffbGjRsSieTYsWNJSUnhcLi6unrDhg0NDQ21tbVnzpxBjsT35fF4mpubT506JZfL9+3bd/​r06VOnTkGDCQQCPxLiR0JYrda6ujo0ZVAtFQoFBxLmDTU1NadPn25ra8vLyyMeGUitubk5MTFxy5YtcL04JltbW/​fv33/​+/​Pm1a9e2traiCwmHw5WVlcPDw1Rqst1zc3Mx0nj55ZebmprIn5mZmeE8xqNJCLF79+4jR47I5fLp6Wn+aRzlfD6fSqWCcbBz587m5uYbN26w1urt7SULhYGGkAaz2Tw5OXkn1ca7hf72Xg0NDampqb29vaCuBoNh3bp1ly5dstvtRUVF8/​PzQLeRWw4qMApiYmL4NUlJ7FelUmlUV8WQm5eXh8VK1LqSERhjZHperVbrcDgoqVNTU9RoCGrR/​gjEMzk5GbRnYWGhtLTU7XYz/​o+Pj4OZaDSaubm5oqKiy5cvx8XFtba2Tk5Ozs3N3XPPPYODgyzWtlutB6XSYDBoMBjIiKD2ge2Gw2GdTsc5x3qT7TEOumgCIOrJ5XLs5sfGxnBOBloZHx+32Wyw6YUQQL2w7vjrbHdBxkGKYLygHop+qlqtFhYgtjMEppN1TqsLLRJ4nTgOjOSEEJwWer3+oUuX/​lhYyPgC9RvcXKfT8TEODAz09vZylNL5IhfCmQ47B7bcMzMzzz///​Ouvv/​6Nb3xjcHBweHhYo9GsX78eWwI65ZKSErfbXV5e7vf7W1pa4Kpz3tPk8of37dt35swZ2D7nzp3DI54OQK1Wf/​TRRy0tLdjFcIBxzkF4ffnll8+ePRsMBs+ePSuEqK2t/​fzzz2UyWV1d3YampucnJjIyMj788EOWwLTV5JcJIWjY16xZc/​bsWXw+ID7u3r0bQa8Qor29XalUYlmcl5c3MjKyYcOGtWvXYpMwNzdnNpvByrHjTk5OttlsSUlJfKH4qRGtI4TYvXv3sWPHNBoN+thgMHjx4kVi5emKXn75ZYRXgPX5+fmZmZkFBQUnT54E3ENpyIuvra1tbm7mjxG+difVxruF/​vZeBN8wlg4PDxPBU1FRQeAyfTrUXSEEAijMZxghox6BNPuYewD4LF68GBdy+lMaapvNlpaWBoyODTJALbSKlJQUcBtqLoIap9OJRMXn86WkpFitVlTvFosFOBWkiFKiUqlcLldHRwfZEWazOT09nSw6TBm/​29//​h7y8cDhst9vPnz9PXDWoK2QeGkCwUWZzXH/​9fj9oKSIADgaZTMYuF0cBuVy+sLCQkpKSnp5ut9tXr14NmozkFYwLG14ER0w/​CoXCZrMxEJCPiHKHjCcsNgnx4KexUeDf4jUjbQNJy8jIoIscGxv7Tm/​vu6Wl0c0Kx61UKnU6nQaDoaen52tf+5rNZoNJ5XK5vvKVr1y5csXlcnFgM+HBdlUqlR9++OHDDz/​8/​vvvc2YAQCmVSki3MTExzz77bGNjo8vleu65527evMlCGLSBrhz8oaWlBdaNQqG47777GBChVDmdzqinEJ6per1eCMGsIJPJcDdDbdTY2NjW1gZQc+rUqfpI5Hc5OYODg3q9PhQKPfroozab7aWXXmpsbISzROkkYwTeMN7OvB4y3Bkpjh8/​znlfX1//​y1/​+ktEWmhDWT+Pj48xDO3bsuHbtmtvt3rt3b2trq0wmy87Oxj56fn4ep35+bExMTHp6OmY+RLRHIpHjx48XFBS4XK7S0lK73T4xMWG328+dOweuWFVVxfgCFocpArecXC6/​w5Sxdwv97b3Q5uExCWcGtSGRIICMtEW0vaRwBIPB1NRUXBCmpqbkcjn+AaDYyGKDwSBUPDiR0PCxngevoI0Nh8NOp5Mfi7w+GAxqtVrqICZiuBrQ18tksszMzL6+PrgN+AdQDjhjogxlHidss2JjY0mS2unx/​ItWC31o/​fr10XRANnJqtRqKi1qtXr58OY8o8Ah4CEzHUChkt9vtdntBQQFQUmZmJhRGi8VCI4xhL8cPLp6cl4j4IZXz0+RyeVJSUmpqak9PD6pjWJ7ESPEpQZdUKpW02xRieCD8ARYJCoUC9+BIJKLVah+5cuWvq1ZFlcnMQ1RPt9t99uxZwpsSExOrqqpkMtn777/​Pp7qwsIAjPM5cbCaLiorwHUtJSdm5c+e6deu6urpGRkZonBcvXnz58mWXy5WYmHjq1ClUBUwwVD3gbxpnEJ7Y2NiOjg6EXUIIkPoLFy7s37//​8uXLzHycEzU1NefOnYOrMzk5iRMyr1OpVC5fvnxycnLfwsL75eV9fX34CXd2dqrV6mPHjoF4rFixwuv1bty4sa+vLycnh4Sy6DKT25UGBa9p+ncscaamphYWFiD27N+/​f/​369efPn6cROXPmTDgcTkpKIlGZ18yMAiUBW1MhhN/​vZzzCtc3lcpWVleG5HQwGR0dHa2pqNmzYsH79+pMnT5I4+OmnnxJCyxyJfGzt2rVdXV3cA3dSbbxb6G/​v1dDQwF6UW8dut4+PjxcVFdEpY2m5sLBAfaF9g6jA7avRaGBhYh4JAI2c1el06nQ62AVxcXFdXV02m41CRjMY5ThDMEB5mJiYiIwTOeXo6Gh3dzcZzTTd3PRFRUVk7xENASWD/​bDFYhkeHjabzXl5eUirRkdHwUNjYmL+YXz8nxIS5HJ5Tk4O7zrqWTY3N2exWM6dO5ebm9vf30+I4NzcHF0wZYXiG4lE2tvbN2/​efPPmTdYGdrs9EAikp6fPzs4+/​vjjY2NjKGgUCgWKLeoCbxYwCodORK1KpfLmzZsYJsOzhifDOIWDpkwmA3RmO61SqUDegQXgHfFi4KVIJJJH2to+XbECSRocISgoMLKLiory8/​OLiopKSkreeustqVQ6PDzM4eH1euGYYmUcExNzzz33fPHFFwjHcEA7deqU1+stKSmZnp4OhUIvvfTSO++8w+ml1+ttNhu1dX5+fv/​+/​b29vS6Xi6wrxhqqPwt5TqlwONza2rqwsNDU1BS8lTOclJT04osvHjlyhNuvqakJ9gtOMtxpw8PDFRUV24eHn7Ja8UPFeigYDC5duhRrIKvVKpPJOjs7a2tr//​KXv3DeAMQTMCuECAaD0Xe9d+9eXkZVVdXx48dVKtXOnTtPnjzZ2tra3NzMDbN//​35s4mdnZ9mdsEehW0c6Ho2QhIvMPfDMM890dHTggsc0HBsb29fXd+rUqVOnTnFIM9xUVFT09vYGAoG8vDx26VNTU9Hh7E6qjXdZN7f9glY4OTmpUCjKy8ulUumxY8fw98BUklpPQx11fIRWb7VaXS5XXFyc1+sdGhoCxpHL5fTs8/​PzCQkJkUjk+vXr4XAYqxx67aGhITCB6elp7DDPnTs3NTXlcrnY+nZ3d8tkMuo1sz8Sc7amSKIkEsn4+DhcTKlUStry1atXYdf4fL6WlhYaarxHonYIMDipsC6XK3IrBWJiYmJubu7GjRv89eiYjAVKXFwcApmxsbFgMPjXv/​4VpDs9PT0nJ6egoGB2djYvL+/​Pf/​6z2+1Gf8upNjU1BYkQmiYfkVarJYabpQUe0awx5ubmZmZmAN+RC3HIqVQq3r7dbse/​ISMjg9EeeT0/​GSTn/​3rGsTPA+yUhISEpKQkyDwcevBE843w+H6YLCQkJ3/​jGN1JSUvgGgcsx/​f/​KV77CBw6ndm5u7v77779y5YpOpwsGgzt27FhYWNiwYcPFixeB5l599VWbzVZZWRkMBn/​wgx84HI7t27djfTo/​P//​cc89hmMqpH6WKc7hOTEw0NDTQQOAW6fV69+/​ff+TIEYvF4vP5EhMT9Xp9f38/​NzOfQGZmpkQiycjI6Ovr6+3tZWPEICWEiJJ5pFKpxWLhs6U6KxSKHTt26HQ6SFCwGKnFr732Wnx8PEZ1OMUfPnx43759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SwRmxyaLTjYJCcnx2Kxn//​85++++65EIlGpVD09Pe3t7RgJHDlyxGQyHThwAMGQiOlARiMhRCAQQE6RmJh44sSJ0tLSaDRqtVqhpEtOTqbT6VlZWVVVVeBB4gcihNx33305OTkvvPBCfX09hjRwU+jt7V1aWsrIyMAnnJqamp+fv3LlitVqBekQXFvILKA8AO6xuLioVCqTkpL4fD4cQLHrazQamFzS6XR85qNHj1Kp1KGhIYSM43fEzBl9w+HDhwkhdXV1sJyDmxAhRKvVQta0d+9eFovFYrGo13IHkXyJ+gBWGYSQWCwGZ+OjR49OTk6CH6zValNTUwGHQsX24IMPApeD7QTm/​263++GHH6bRaBKJBKW6w+E4evQojUY7evSoz+fzeDwvv/​zynj17VCrV8ePHY7EY6vr45PnrdNyo6K/​vgUhMl8tVWFjY3d2NQAyYvINvXlBQgDwQLpd75cqVcDisVquhKkS9gzwQQggcxJBOh6wSkLt5PB78vPCauGcQiwzyPu4cbC2BQEAqlSLDNikpCSpHDoczODjIZrNRc6Fyj8Vi2dnZ8BWA3QIQ51AolJaWBgl+7JrBstPplMlk4XD4SZerffNmzNxQfwUCAYPBMDs7i60IpTGWGLg5UqnUK1eurFu3TigUtre3x2Kx4uLi4uJio9GIdueHP/​zh8ePH9Xp9RkYGnU4vKiqSyWQff/​wxkNZ77rlnYGCguro6LS3NaDSWl5c7HA6NRvPhhx/​u3r0b5oVqtfry5cs4z6AnJicng+QOGjVGDijwMYuDoRsU/​FgWPdcOqO0jkcjWnp63U1N9Pp/​D4YAZUXl5ud1uX1hYWLduncPh8Hg8NTU1Go1mzZo1GRkZ7e3tIFPhbOTn5+fm5qLPs9lsNBrN6/​UCyQG9EuBYaWlpf3//​xMTEgw8+aLPZIEs2m83IbHG73X19fVVVVdBq2Gy2devWRSKRgYGByclJ7FXQHMnl8rm5OfxkoEuFQqH169czmcy5uTnI98C4R4dHo9FUKpXRaAR7cqfTqY3FCCEYqyLgBUA/​+qr29nZCSENDw+XLlwOBAIvFOnPmTFtbG7Z8iDzQe4XD4XPnzhFC4PoA9w7so4DsxsbGIKSqra09e/​Ys/​DygK3z66aePHj3qcrnKysosFguupfb29mAwWFtb29bWhvCTLVu28Pn83//​+9zfffHN/​fz+CTcDbYTAYu3btqqysXFhYMBgMGzduPH78+KOPPqrT6WCjxmKxMKb+Oq2NNxb663s0NjbefffdNBoNXk7glgB29Pv9P/​zhDz/​77LP09PTz588jFTYYDK5YsWJ+fh4YMUoYLNbQxyNwClk5vb29SEEym81gBxJCMAwEyW9ubo7D4YDeg0ocZAmUTiKRCOqe7u5urOkCgQBNAEotMKlLS0svXbrEZrPT09NBlJTL5TANhus33lGn03G53F0ezy63GzJ06Iza2tqqq6sRlwo0CahRTk6Ox+ORy+Wff/​458gInJydhABkMBi9fvhwMBhUKxdq1a1999VW5XL5q1ar5+fmMjAyn0wl8Fi5A0M0CyVm3bh36lY8++ui+++7T6/​XQMQEBEIvFXq+Xz+djQces2Gq1SqVSACN0Oh1fCqZyEokE8ALEtC6XKykpCYYT8FrZ0tX1+4yMnp4ei8VSWFhYU1OjUqmEQuHs7OzAwAB4PpABBwIBs9ksEAjgM4M6+nvf+97i4uJ77703NzcXCoWysrIgnopGo93d3Xw+3+v1yuVyaBSSkpKGh4f5fP53vvOdzz//​3O12YwWPxWLYSrHrczgcDNXNZjNY6vBIQKqt1WpFcY3RMWjmMNBWKBSQYYNUg7RezFqQKa+NxfbTaEwmE8gbjB/​ggslmsy9evBgIBNhsdkVFBaiK6COBtODqDYfD9fX17e3taCwIIegt9uzZc/​HixWAwKBKJCCGg/​RBCamtrn3/​+eWwGe/​futVgsMOxUKpUmkwn7qMvlKigoQJo8gmLgLKLX600mE/​g2fr8/​Ozsb5tvowM6ePXvp0iWNRuPxeIxGo8vlOnPmDAwncJbQdH6d1sYb0M11P5qbm8PhsFgs1ul0paWlKpVKpVLRaLTq6urGxkaNRnPy5EkajYZZZSwWm5iYAAYSD+eElwhuAJTqKpUKLHhIZFeuXMnn8+NqQNAuYS4fjUaR1g3TFUQugM6PRdBms23atAkgJkiKAC4AznK53D/​+8Y/​f/​OY3kUTodrs1Gk1HRwfitrOyssrLyxkMhkAgyMrKysjIIIRkZGRgTcf0GFPciYkJq9XK4XAQ4rF582Y6nQ7bfVASIedhsVjz8/​Nmsxnj4ocffvj06dMsFmvlypWAsJxOJ+BjfBfckN/​97nenpqZuuukmoAddXV3QxIIqLY39jgAAIABJREFUGggECgoKampqIIIfGxvDNhkMBlF4IlkQZCG3252amop0F5jD4Kxinsnj8eBj/​PHHHyO/​cO3atVCEgqNy8eLF06dPr1mzBu5Gt956K5vN3rJli8vlmpiYGB4e3rZt286dO1esWLFmzZpz587BeB24TX9/​P/​YbmB1BvGOxWJD+Ae6K3W7/​xS9+gd8F/​jB0Or2goACmRoCGlpeXHQ4HwDekiQEeEQqF2KoJIRQKRSwWRyIRqVRaW1sL381nn30WqjG4GszNzU1NTYFBBIpRVlZWNBqFOxiioABFAnYjhCwtLTU1NaEBItcm1fD3h+Tq+eefh4KPEIJWgEqlNjU1wU7H6/​UGg0GER/​r9/​mPHjiHXm8lk/​uEPf4Adhclk6u/​vhy8Ii8Xavn370NAQMrBMJhP9WtwuXDosFsvg4ODCwgKs8bC/​wnAiEon09fVBXgf+rsvlMhqNBQUFKSkpa9eu/​Z9eJq7zcWOhv+5HUVER9OUCgQDEQYwxg8FgaWkpbLYKCwvXrFkDjkQkEuHz+WBqE0LivmAQXgIyDofDsOvKyMiAuspoNDocDrVajapQIBCwWCyZTOb3+zFmxMQMvoAYvQJRCQQCFy5cQBRJnN0My0AqlYr5HioseIvbbDZM2BYXFwcGBsRicWlpKYosNO/​Ba1GIHA6nu7sbwA5a5qWlJWwVyByPRqPHjh0DJGW327OyslJSUjIyMiQSSUJCwu7duz/​55BNgCL29vUBXEOQNcp7JZMIpam5uNhqN+Gowza+srISxMBga2G8sFguSMYBWwTSYx+OhQTGZTFDchMPh5OTk5ORkaOVhJRSLxeCW09nZ+fvf/​16v17/​44ouEEJ1Od+edd0YikZGRkcHBQUIIZF9FRUUCgaCjo2N0dPTSpUsOh+Puu+8uLy/​v6ek5duxYIBAA8ebYsWPYibFuQrNKCMHi2NDQwOfzz5w5QwgpKSkBWoLN0uv1HjhwgM1mp6SkiESikpISkUiEMCbslFwuF/​scamqXy4V0Q3xlFouFyFyLxXL48OGEhASZTHbw4EFMv++5557+/​n4QVNDH4Er2er0FBQUGg6G+vh72n+ifoHVCMQEprEgkgnEbHsdUFhQmCoUC0BzdEoj8jz322H+9cUZGRrhcbkFBAUa+2LTgDoT9G37L0Wj0iSeeAP0GtEv8ZIQQBoOBRRw7d2FhIfY8YPqEEIlEYjabjUZjSkoK2GKXLl1aXFyE0ebX6bix0F/​3Y2BggM/​nv/​POO3Q6fXBwEH5kcDUZHBycmZnxeDwo4dFNT09Pf/​rppy6XC80mhJcodaFQRY7a/​Px8aWkp6NKoU8rKyqxWa0JCAioj9AFJSUlQ5OKeYbPZMD5Dfvfq1asTExOzs7NR0cPPMhQKYWmWyWRms1mlUsFRBEquubk5hUKB1iEzMxNWuliqcBchMATpP6CIjI6OQiuUnZ3N5/​MnJiYATIlEouXlZYFAoFQqYbfCYDCmpqbAC2xubh4YGIA2JyUlBaoWlPNYv0ZGRgAuuVyum266yWQygWqdnZ1NCIGOFxPIiYmJYDC4devWUCiEnN7e3t7s7GzgCVCTpaamzszMOJ1OEChB4DMYDGazGSNK7KAIckIZTggBv35oaAgdCXa4qakpHo8HfveePXvAfF+5cmXceA47PYplKpUK7yC5XN7U1HTo0KF169bBF+Hjjz+GcSY2MAwYEFEABdbOnTsNBgPEnxiBwDIzNzc3ISEhMTERWB8M7JxO5/​j4+IoVKyQSSSQSwbwaS+3u3bvh9wlM/​9KlS0wmE8pbOFDCSCcajQ4PDy8tLR08eHBwcBBwP1ZzjHBQAcCILRQKNTQ0oG6AfRjcFAghZrPZ5XLV19eDFUalUn/​1q19ptVrM8yEmwDbz8MMP9/​f35+XlJScnczic7Oxs1ExWq/​XWW28VCoVNTU0MBuO1115Dh4fJ1uTkZENDA+YQ2O3EYjGdTr98+XIoFFpeXj527Bg+eXp6ety0Z3BwEHwHYHf/​lLXi+h03MPrrezQ2NnI4nEgkolKpUBI6HI5du3ZdvXo1KysL9rmQTcEwAEtYYmKiRqOB9RKwdRQycRH5/​Pw8nU4PBAKAj4HqhMNht9uNGw9FH6AGl8sF7Yzb7UYOxvz8vEgkAuifn58fDocdDkdqaurU1BR8AZOSkhQKBZPJPHHixO233x4KhRCq5/​V6V61aZbPZQLHv6elRKpUzMzPhcHj9+vUFBQX39/​f/​WioNBoMwuDeZTBAu0mg0oVBotVoLCwuh3aXRaMFg0GKxQHhVXFw8MjJSXFx89epVGo1WWFgICRjaf9hhJiYmgpyH2Rpya6VS6caNGyG0waKMpR8OM4jXmJqakkqlFy5cmJqaolAoZrN5586dJ0+ehKkcl8tFs5KYmKhUKsGSxHQEiw6cFFks1tLS0tWrVzdt2rR69eqsrKxvDg8fTUpyOBxUKvWvE7sIISwWKyMjo7CwEMpYJpM5Pj6OiS7YgZChAWvCELK6urqiogLNGWwnfvzjH7e2ttbU1EDiPzExgbUbwxvkaUQiEYVC0draCrcc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hYuHv37mQyiRsVpFOnw+F/​zcl57LHHiouLcfPDECyTydTW1g4PDwOMDOU+8Eezs7NIIIBYC98CiUQCEzjISEBsXrx48ebNm9AaPP3003/​6058OHz48Pj5uNBptNhuwH3Nzc08++SSDwUBnQBAE9Mr4P+7bt29kZGT79u2jo6MA9qVSKUROIldZJpMlEgkEFpJIpPHx8VAohLGq0+lkMBhr1qzx+/​1tbW1r1qxBXtW9VBvvL2Pv7kMikerq6mA6Bx3MYrHodDq4kyAKdLlcfr+fxWLxeDxEvCLSSKFQQEuOXhUBTFjQbdmyZW5uDqoyDAGqqqrAlZyZmQkEAsCTZZloaGQg+HE6ndCe19TUYN+YlQ8iggqeHViWent7SSRSUVER4MaQipLJZEhoCILAb0d9HBwcdDid0rw8uVyOOQPQxGjW3G43TqCpqSmov5EKVFNT09XVxWKxFApFKpUSiUQgz3C53P7+fmTFoYhEIpG8vDybzZabmyuXy5G+kj1E4ZVNfp1SSyaTrVYrqA94I6BPTyQS+MkvXrz47rvvms3mhx56qKOjA9ohsLHAk0in09gKwtIJASsiU5LJJIvFCq6u0qhULIczX4eBAAlHEMTZs2dfeeUVLMbVarXJZOLz+bg8Xbx48Te/​+Q3QK1gmY7bDZDIxIgNZfmxsDAau5uZmvV4PSg8C2UHcVavVHR0dW7Zs+f3vf4/​dxtLSEkEQRUVFmM4VFxd/​9dVXmIY//​PDDxcXFv/​jFLx5//​PF//​vOfhw8f7uzsHBoa2rdvH1SnVqt1w4YNbDb7/​fffxzx969atIyMja9eu/​eKLL+KJRHFREYPBKCkp6evr8/​v9OAMQ8BKJRKCVAvjaYDBkIUg6nU4qlS4vL5vNZrTVa9asaWho+POf/​wwgNsIMMpnM8vIyTKoIjoe3NhKJZPeilZWV4+PjCAsDzamgoAAzTNiJ+Xx+IBBAigOPx9uwYcPAwACPxyMIAldYaJB+/​vOfk8nkkpISwDmGhoZwpUPMlkajgS7rXqqN9zv6u/​vo9frKykrUQdg9kPiaxQ4Dd7OyslJYWCgWiycmJiYmJvB9Q0+3bdu2qamphx56CLQydLsTExOQJUBzyePxMpnMxMQEBtCYogIQiPsygI6YUMNmhZtEKpXSaDRUKhVgAEQYrq6uGo3GU6dOffrpp/​n5+cgyRZUBegyWJSqVClEKl8sF5pBCoZxaXf05i5Xz9RMOh4PBoFwup9Ppy8vL69evx4YAd3YAHhobG0HE9Xg8bDbbaDTC1hsOh/​1+v9VqDYVCfr8fX3hIlQCXh7ofQyqMO3BfwfcTu0232/​3CCy+UlpaCAAENNVagkAzh6gDRES4H0IZiFXHu3Ln29nbAIRC/​FwgEABkNh8PnU6nXqFT873B2As4Fu+/​i4qLf729ubh4cHPR4PLm5uSdPnrxy5QqNRhsaGnrhhRcg6MRbBgkTl8slCCKRSCD3kc/​nnzlzZmxsrKOjA1X1nXfeWbNmzcTEhE6nu3HjRmdnp9VqhaMVTErkPZWVlV25cmVpaelb3/​rW8PDw5s2bBQLB1atXr127ptForl+/​LhaL5+fnR0dHaTQa1vLpdBo+ZI/​H09raOjo6ury8/​O1vf3t8fNxisTz++OPfGB7+rVRqMpkAVRUIBA8++CDSDhwOx7Zt2/​Ly8vx+/​5YtW6DURHhTMBjct2+f1WrFpQozRrvdPjw8zOFwjh49Wl9fDzUqWJj4a+Illclk8NCeP39+YGAAZyTGXMePH8dEC4oAbOZhOoM4Cnvs1dVVLNvD4TDEr2Kx+Pr16+FweNu2bQhwdzgcOBQjkciJEyeQrHLvdfT3C/​3dffR6fWlpaU5ODhZcUM6B/​ggUO1SGNpstHA4vLCwoFIqKiorV1VVMPCHOw4hGKpXabLaqqiqj0SiRSLCR83g8uMaCIAaPO75puDIXFxen02nMWzZt2sThcKRSKRrtrVu3IvwEF3yHwzE+Pv79738fa4CPP/​6YRCKB8QKGMBBp+EfMIkA5HxgYMBqNOp0uEAicDoffVavRuVOpVLPZjIl5Op3Oz8+fnJxE5gnWBoFAAMIbn8+HpRw21RBOABaWTqdhKULWrkAggJsUowAMtQmCgHwwFApBro7WfmZmhs/​nz8zMLC8vKxQKRLGHQiGfz0elUjH6AJAdxQViU4IgkFIEzTsINkBF4n1B8hGCR978WkWTSqVw9wLJJxgMAgnQ3d0djUZzc3Nffvnl119/​He+U2+1GMmo8Hr948eKNGzcQKoDRTU5OzpEjR/​r6+iKRCJyu+/​btg3b28uXLnZ2dx44dw/​4GRlO1Ws3hcHg8HgbNGBY98cQTc3NzPT09DAZjZGQkHo9jTohOORqNguibn58P6COg0BwOx2azdXd3wyY9MDCAnMKampqHBwfHv/​nNrq4ujFkKCwuvX7+OzRODwcCYpaqq6tatW6FQCDdRCoWSm5s7NDSEUECYJw4dOnTnzh2AWv/​xj38sLi4WFxfn5ubOzc0BlAZENhp/​q9XK5/​M3bdrU3d3d3NzscrkikYhGo/​n4448RoksQBOxO0IDitAuFQlVVVeFw2OVyabVapAPi/​MZCuLW19cqVK6lU6uWXX66vrzebzUDpYQTH5XLBgr6XauP9Qn93H71ev337dsyF8WWmfJ32SRDE3Nzc+vXrx8fHI5HIli1bRCLR4uKix+NJpVI+n29paUmr1dLpdPDicSQQBAFSYyAQqK6uZrFYBoNh3bp1ZDIZ3qJdu3YZDIa8vDzYcQHSgrEbbf6uXbuWlpZIJNLg4GBJSQlEyouLi4iUunr16sLCQn19/​erqamFhIZrcWCz24IMPQoyPAwb9bzqdhmU3mUz6fL5AIHCRIE4GgyKRaGFhAV0zj8eDMDSTyUAjgahuiElwIWhsbMRqGlQDsIJdLldZWVlubm5lZSUCyjF9HhgYALeSTqeDzAzgPriTAScAACAASURBVJp6giAwKWKxWN3d3TAYI0O1o6NDp9MBAyAQCDKZDA5FfPmhhAHPHXsOvFN4AVOpFMLw8G/​S6fTVq1d/​QhCtiQQsr6AmQLWNuxfsEdDds1istWvXwtuF/​+/​BgwchPerp6UF8Co5DjMWvXbvW0tKCqHHs27lcrkQi0el0Lpdr165dnZ2dOB1rampsNhvmEisrK9/​73vcIgsC951vf+tZ3vvOdr776CgUOcfCbNm0yGAzpdHp+fr6jo6O0tBRTmsHBwbNnz169evXs2bNXrlyB2N/​v9x8+fLigoOCDDz44l0gcmphgs9mdnZ2HDh1C+w/​HH4fDaWpq6uvrW1hYIJFIIpEIaNXS0lLQT1dXVx988MHp6Wmv1wuD1crKyg9+8IOvvvoqFotBmpWbm4sOoKKiAvmIWaIkImThL+FyuU8++eTY2BhUxcFgEOIZGKRxylKp1EOHDrW3t2d1WXw+PxqNYuyZTCb7+/​tx6nR3d7e3t2Nzg64Io1QYfe+l2ni/​0N/​dB6NVLE6B+8h8nbyKJEz4pIxGI6p5fn4+gl5x31SpVJhskkgkrVYLLLDb7cb0Axd2pVI5MDBgt9stFkt1dfXk5KRWq1UqlX6/​H10z9qV+vx/​BUmaz2WQyQWfpdrtxUaivr19eXvZ4PKACZAEDWDzCRYLLNfj1QBz39PRUVVWBSOVyuWQy2cnV1c+qq1OplNPpJJPJuKrn5OQEAgEajba8vIzWHkxEMpmM16Svrw/​BT7m5uTMzMzU1NUAC0Gg0i8UCxNvatWu3bt06NTVVW1srk8kwDEHdDIVCWTwDLiiYxUM9DRXp4uJiPB7HkQOSPn4xdgm4H4BFDEkSk8mUyWTAyWEgA0uUVCrFYFqpVJ4IBH4lFIbD4erqalzUvF4v7hPV1dX4vUKhsKCgYGlpyWAwoDCh5o6NjSUSiddeew2CHNRHeKchPN22bVt/​f//​Zs2fhlcVPGwwGsWhFiA1eSaVSaTQaYSRGLComdaOjo/​Pz80qlkiCIpqamdDpdXl5+9epVgiAaGxvNZnNTU9PU1FRnZ6fL5UqlUgClwWeXSCRgDpidne3r69u2bdvTi4uvUaknT56MRCJXr16FCAreVIgss8mR+Jz4/​f7l5WXsb4D6wZAnFos999xz+GORywhKwY0bN4DfWVpaotFoeF+ghoQ4CgtwgUDw5ZdfwikCH3IgEEBdxmwNbX53dzefz9doNCQSye/​3I06EyWRGIhGlUikUCrVaLRzXq6urmzdv9nq9DAYDtoyioiK4he+l2ni/​0N/​dR6/​Xl5WVwR2OCBGgPygUyu3bt20228jIiMPhwN4P0R+5ubkul0uhUPj9foynIWQUCARDQ0MlJSW4LOfn53O5XERd4/​OqUChoNJpQKIzH4+l0enp6GkxEoVCIwREKSiKROH78+MzMzPr16+F4ysvL6+/​v93q9Op0O38lMJiOVShGqCSggksphVpydnZ2amrLZbPF43Ol0QhNdUlIiFAqfs9kupFLIICQIAvUiO8f3er0SiWRkZEQkEmHqAqXpvn37mEzm3NxcMBgESYbL5fp8vuXlZfjCMIVns9kSiQRHFACQ2PjhVIBIBhFxIpFofn5+fn5eoVDgbw3kJDTaubm5RUVFJBJJJpNBiYSVKTADOFaFQmE2LY/​JZAIPGY/​Hm5qa4GgNBALnUyk9QcDugJQoGo0mEonA/​kTzCB8WUPK4dqTT6ebmZtBJkdJHJpMLCwtR4Mhfh2ijE4cWBQi2PXv2DA0Nvfjii++++65GowH+LD8//​8aNGy0tLYuLi+idodNtaGhA2C/​MyZ988onVaoX/​zmw2b926lcPhXLp0aWlpKRqN1tXVMRgMTKvNZjOwa/​X19Xfu3MF2lMfjPWezDTz88H//​938jCAzM5127dn344YcajWZoaAjAIofDgf8jFtoIjQE1AffIwsLC9vZ2xNHAopxKpebn5yORCJobOp0OqUIsFkPfgz8Bc6dMJlNdXT09PY0D2Gg0QkAMZSd2Eng3YYPKtlZnz54dHR0tKCg4dOjQRx999PTTTw8ODqrVamhh6XS6TqcjkUijo6NouVKp1L1UG+8X+rv76PV6jUYDSTsUewAA9Pb21tbWrlu3DvPxLI8wPz9/​dnZWIpFA2QLhfH5+PkQOaHhhv4QqA/​GhOp1uYWEBthE4gCKRCNZTDAZjbGwMUhA4j4D6gv4PykLcZJlMJjZdFApl27ZtyBHdsGEDIp5hSLHZbG63W6vVqlQqNpttsVjWrl1rMpkymQzGRE2BwE/​pdC6Xu7S0BDg4Vs1ohzUazezsbE5OjkajMRqNGo0GcwOr1SqXy1dWVnAcwlwTjUa9Xi8MR2KxmMFgQGoJpRDacIIg8FeGMg83fXzPPR4Pj8c7ePDgBx98UF9fD4EpziqgodE8EgQBzQakR+vXr4fMEag4QN+i0ejNmzcJghCJREiFxNrjTDT6Bp2eTCZbWlo++ugjdPp4uZqbmz///​HPQ5LHBxpUF711nZ2dTUxMitrFoBX334YcfnpmZ8Xq9MF5gPc7j8TweTzqdtlgsTCazo6Ojurp6aWkJrojp6Wk6nX79+nVcVioqKoxGYyaTMRgMPB6vrKwsGAwiIsZutyOn5cSJEzdv3hwbGxOJRDt37nz88cffe+89vEcEQRw5csRqteI4j8ViBEFQKBS73d6aTP6cxYKAEi4Et9utUqkoFAoSPI4ePXrp0iXcS4RCod1ux0CsoKAAyU0IVCgoKHA4HEtLSzi5YdZ76KGH5ubmsCwB00ahUEA3CalMNiQLOn28/​nhJLRaLRCJZXl6m0WiTk5Pbt2+32Ww8Hg9rklAohOCw4eFhAIVWVlaKioo+/​PDD48ePG43GrC8BdwIqlQqb9z1mmLovr7y7D4lEevTRRykUitls5nK5TCbz6tWrkUiktrbW4XAUFRUBCj83N4cNJ0DtUEFgOANXJG6+LBZreHgY+ze/​3+/​z+Xg8XiwWA+oAOzGHw7F3794vv/​yyuroa3MrJyUm5XD42NhaPx4uKiths9p07d2AgEovFOIGghCsrK5uenkaCBBgvsAIhWI7FYuXl5eF7BX2hwWDYvXs3uGaBQGBhYcEfCNRUV6fT6eHh4Q0bNjAYDB6Ph9+LakUQRCgUws+GQfbCwkI0Gi0vLx8fHycIQiQSwejkdDpFItGaNWuw0IMcCKNbmLAymYzb7cZRhzYfe+ls2i08Qdu3b799+zaQy7FYrLa2tre3F3wx9HpgNeMsFAgEQGZiqYuzBPsV5G1hZnX69Ok333wzEo2WlpTMzs5mMpmSkpKZmZnjx4//​7ne/​q6ysjEQii4uLCBmH5g/​uNoj/​AoEABkpIG8d7gQUyrP81NTW3bt1CZgjWAzk5OZs3b4baCqvX/​v5+iUSSNU5jRo9hFJfL3bBhA4fDGRkZycvLGxsbKywsnJubw8xQJpNZrVbkMiK+PJlMwk2Gs23dunUDAwMVFRVPPPHEr371KwqF4vf744lEvkwGnSuknFjqrq6uFhcX22y2srKyvr6+RCIhl8sBtXe73XK5HJ41fEIgn0WvTaVS5+fnL168+Oc//​9nj8ezfv//​999/​HB8bhcBAEgU4FH34WiyWTyUwmE7Y+TU1NP/​3pTwmC4PP5Kysr+CDBUOZ0OvPz8z0ej0KhQLY4rrmIGMSlAddBt9sNCA/​GRwKBgMfjGQwGAKMcDse9VBvvd/​R39wECAVUD02qDwUCn0x988EE49cHIBam1v78/​GAxCXAx5JZvNBgCyoKAAakh8z0HfJQgCDh0WiwWYzMaNG41G49jYmEKhwLjAZDIh7aikpIREIqGHFQqFHo9HIpHk5ubCNpWTk5OXl+d2u4PBoEQisVgsZ86cCYVCeXl5YrF4cHAQrS6ZTEaUOYlEcrlc27Ztu3HjhsPhyBa11lTqN3l5fD4f3+RYLDY1NYX15u7du3HV0Gg0Ho9nYWEB+hOAurq6ulDi165dGwwGl5aWcP/​w+XyY/​8JJtLKyAsYyKhquIJAzoSUnCALNO6AxUqkUwBkUQZRFTGNQRIDhhbopkUhgDAXuSnNz88DAQCKR2Lp1KzaukUgEwYo1NTWdnZ2tqdS7ajUaQGxWDAZDKBRCAADGvrhwkMlkrIjx06LeEQQBFRaWz5AbwSI7NTUF5ByNRsM1hcFgzMzMJBKJioqKVCo1Ojp68uTJ+vr6sbEx3JZCoZBIJMJ7DbsptrXwfyHQMZVKHT16VC6X9/​f3g5OBQwL/​X9RHRLV897vfvXbt2uDgIDRFmUzmXDJ5JhyGDr2jowOgZixCDAYD7i5I7yMIAgcAl8sFI14qlT777LNer9fr9aJ6openUCjt7e0QzsL1xufzCYI4ceKEWCweGxtDZ4AQ+eXlZZD9T548+frrr0MIiyadwWAwGAyfz4c8yBMnToDjTxAE7g2QikFHANcb7kNQ5oRCISqVumbNGmDXdu7caTab78/​o7z//​g0ev12/​btg1uWGhppFIpWLjI9BgaGsJ1sr+/​Hyl0qVQKjo/​c3NzV1dVAIFBbW0sikebn5zE7TiaToVAI0m8EWRiNRrPZzGKxnE4ncuwQAsVkMgGxyc3NBWAWiRZQjxEEkUqlIKSTy+WJRMJkMiEhhEwmz8zMxGKxUCg0ODiIno4gCJwuKpVqfn4+Pz8fiRxFRUUEQYA9eTIYvJjJpFIpgUAAZZtGo/​H7/​fn5+QRBjI6O5ubmbtq0qaSkBCJroVAIzkwW4x4MBqHZh77FbrcjwgniyyxUFkobqCShqcAsG6cLbAEYFmcZihido19DzUWht9vt4IWhnp49e7azs7OlpeXf/​u3fMArftGnT7t27P/​30U1SWZDJ569at5ubmxo6OE4HA6urqhQsXLl26BIAMiUSqrq7u7e2Fpvb8+fNtbW3A49y6dQsVByYpRKriRYAQBQhPTFHwScDfsayszGg0ohcG9SUYDM7NzYEPwefzrVYrRnahUGjHjh3gWgN+B37GAw88gOAXrPf3799fUFAgl8vdbjc+FUVFRdCnwpU2NDSElw7Tm3g8fjGT+d95eX6/​v7GxcceOHZ2dnUD7FhUV4azKYiZjsdhjjz1msVgg/​4XXYWhoaGFhgcvl4uQ7evRob28vZmj4pjgcjnA4vGXLlng8fuXKldnZWbhP4GdOp9MPPfTQ5OQkfiqsT4qLi7OkGqTipNPpqqqqv//​975BpPvroo5AFA+DKZrPxGi4vL+Nwxf+9oKAAPNfCwsK9e/​d+9NFH3/​72twcHB++l2ni/​0N/​dR6/​Xi8ViFouFvqy7uzudTpvNZoIg2Gw2pHXZGz24u7A4FRUVoVMjk8kLCwsYmGCADtsqZv2YqwIsAxkfOAqgGsAOCusKMh8AU0RRQOIgzIc+nw/​cduwPdTqd3+8vKirC6YIuDJPQSCRiMpk0Gs3Y2JjVauVwOKhKFAolHA6/​HA7/​d2GhUqmELQDMXjhRY7FYQUGBRCLp7Oycn59Ht46+Mov3Ao6Ky+VieZuTk4MZfU5OzuzsLKiZGzZs8Hq9WGuz2Ww08pC4ILkFaH6VSjUyMqLRaEZGRpC9rlQqIRXFwgOSUJVKFQgEysrKUJJisdjt27c3bNjw2WefofjG43Gfz9fW1obNwfnz57Gl7Orqao7FPigvP3LkCELG5XI5lJdITEwkEmVlZW1tbRglt7e3Q02PyCQcSABRhEKhY8eOIcAWpRmJks3NzdPT0/​F4HDsMnU6H/​QSMwXQ6Hf81Go3a7XaVSoVzy+v1bty40ev1HjhwYGJiwuFw1NTUfPHFF3K5HFFlf/​nLXxCYhbb37Nmz7e3tMplscXER/​LJAIFBaWvqDH/​xALBaDX0Ymk5uj0dOhELIAOzs7SSSS3W7Pycmx2+1er/​eFF16Ix+MKhQLiH4PBAMIEdi1gdlIoFJlMBmdvf3//​j3/​8476+PoIgZDIZwBVFRUUGgwEcHvQxsL+p1eqcnBy4K1ZXV6GOhSDH7/​fDJZeXl4e3DxvgH/​zgBwMDA2VlZQMDA2q1Gm4AmG+xWigsLPT5fIlEoqamJhKJgEA3PT0NIHYikbBYLPdSbbxf6O/​uA8MUQRBUKhVT10wmg+kEIMD19fUKhQLNFw6DSCRSVla2srKCjz7ojKCo44qNOb7f78e6kkqlZmVkQNDs3LkTVRuIShKJhH8sLi5OpVIWiwULA5Rp5N6tW7cOPJmKigp0NxizACZTUFCA+Cf8kGQymcPhqFQqDI7EYjHkkkql8pjL9ZNMJhAIJBIJoVAI1yWZTMb8FMz3hYUFkUgUj8fLysoeeughi8UCtAscp7BWCoVCpKGicCQSCYfDUVJSgs0kpquwTYGjgKMI7efo6KhQKJTL5WhmGQzG0tISSicm4FkIMIlEkkqlUqkUw4e1a9f6/​X7UU2znVlZWMIUAkgzs6OHh4ZycHJvNdpEgjjkct27dwsIWd5ScnJzy8nIsh91uN4jn2EOGw+GOjg6hUAit7cGDB81mM07o/​Pz8jo4ODI4gmlpdXQXnEvD3RCJhNpvlcrnP5/​N4PA6HA8eVyWRyu907d+4sLS1dWFhIpVJyubyvrw/​mACQQVFVVIUhk//​794+PjXq937969169fB82mo6Ojrq7ObrdjVBiJRM6fPw8uqVgsrq+vr66u3rJlS2NHB3HxYmdn5/​nz53fs2AGNf2tr6/​Xr17EvnZiYQCa41+sFm89isSiVSg6Hgw3tyZMn+/​v7/​X4/​8r67urqgNaBQKD6fr6Wl5dNPP4UqAXg7KCw3bNgAL4Lf76fT6eFwmMvlHjhwAJQFLPPRxKBpAKnJYDBs2rRpbGzM7XZDiYQ3MR6PFxQULC8vQ0OMiRxeIgqF4vF4sOjeunXr6OjovVQbyf9//​wD3/​gOgDYjqDAYDmzpYPBAOJRaL8Wtgtd+2bRs+u1jhYvxKpVJBXgS4hs1mgw4PE01eXp5IJKLRaGq1msFgLC4u5ufno9PJyclBDpTFYrl9+3Y6nUY+ciAQaGxsDAaDFosFoW6xWAyxggRBIIeaQqEIhUKJRMLhcIAYhE4GqRSQvYOihb8pHGEej2fLli1Zhxcm48j/​HB0d/​ctf/​pKTkyORSFQqFYlE+sMf/​gBWPkg+SMLyer2zs7NGoxH+A7FYLJfL0fxGIhEMdpB6SBAErgJohEFGw7h5cXGRx+MBCq/​T6TCijcfjWGITBIEDdc+ePdiEl5eXw7g7NTUFwTUsPDweLzc3F7MmEol09erVoaGhQCAgk8kIghCJRCQSac+ePbt27cLCoLm5+eDBg8FgsLy8XKFQ5ObmYoEJ9Wd1dTUQ8zAWIN/​R6XT+85//​VKlUFRUVdDp906ZNmUwG4/​VkMok4+C1btiSTSavVGo/​HX3755XPnzu3duzccDtfV1aFRuHz5skKhKC8vh92/​oKAA97mmpia4am0223/​913+VlZXRaDSVSnXy5EmCIJRKJZPJ7O/​vx6g6FAqdO3fulVde+X/​7MEPo+corr1y8ePHixYsEQVy4cAGAfljMXnjhhYsXL545c8br9SK8xePxGAyGpqamd955Z2lpCd63hYWFo0ePMplMYG24XO5bb72FXToW4LAUpNNpACSsVuvOnTvh8/​B6vf/​4xz+wW4K2NZlMymQymUyWyWQEAsHs7Cw+t6FQCC4/​BoNRXl4OsikygQOBgNlsdrvdLpfLaDTm5+c7nc6CgoKJiYldu3YBNnUvPfc7+rv76PX6HTt2fPjhh9CSQ7cLqvvQ0JBEImloaMD4MhqNbtmyBV97ELuCwaDH48nKw7Hiw+oP/​XVNTQ1wBQimQPsMSSVM4YFAYG5uDn2iQqGA5WRycjIUChUUFPj9fjhII5GI1+stKCiYmZlRKBQQyIN24Ha7Gxoa7ty5A7Uf1gDRaBS6urGxMVQ3Op0ulUpzc3OftVp79+3r7e2VyWQ7duwgk8nl5eWLi4vQhlIolPLycsTeEgQxMjLC5XL9fj9wjHw+X61Wo0yDZo5DAsSChoaG0dFR5JQChg63LUZYMM1Ho9HBwUHs8RA0urCwkAXrY/​F4+PDhaDQqk8mQPDc4OHj69Onx8fH5+Xkul7tz504ul1tVVQUZic1mq6urM5lMMCKcPXvW6/​XabDY6nc5isV7y+y9mMi0tLb/​73e+MRiNWwdevX8dwBnAhaOexJ8CqHDvMVCo1NTWFhI1MJlNaWjo9PW2z2VpaWtrb25EfoFKp8Aahxz9+/​Hh3d3cmk7l582Z7e7vZbK6pqamoqOBwOA0NDQg+9Hg8P/​zhD9vb248ePVpZWdnW1rZt27bFxUWfz7d+/​fr+/​v7+/​v5AIHDr1q2BgQGBQAA2DqCPWHH39fWdPXv2//​lJbuzouNHY2NjYSBBEQ0MDQRCvvPJKQ0PDG2+80dra2tDQUF9fX19fTxDEW2+9VV9fj+PKZDLB1gd37o9//​OPp6WmdTme323t6eiCnCYfDBw4cGBkZEQqFAoEgPz/​fZrOBR0QmkzUaDSS809PTYMPBMwwSMgDUWEQLhULASul0OpYlGo0GDPB0Om2327EfRmORjfAViUSFhYV9fX0OhwP4OWAnEonEvVQb7xf6u/​vo9XqtVmu328ViMYbm0WiUy+XS6XTQifv6+pxOZywWU6vVAwMDMpls165dkLSjdsA/​IpFIEPiJSQU+rFCSIAiJw+HAFN7Y2JgtbWCwoCDCXwOmYzqdBktWIBBMT09LJBJw+zZu3CgQCMxmM7K8MZ42m81erxfeImRzc7lcHBtFRUVw00DfRqPRnrVavzc7++STTw4NDXV1dRUVFd28eXPt2rXhcNhut6MHB0A424PL5fL5+fl0Og3fjUAguHPnjt/​vx79xu91lZWVg5kxPTyPdoqSkhCAIsGX8fj/​C7aLRaCwWq6ysRLwiNDzxeFypVK6srPzoRz8aGRlpaWl5++23nU4nZDl4ZTweD9Dt4JWbTCZUvaWlJdhuYUnNycmZmprCUlSr1S4sLJxLJt+g0err63t7e+VyOTyZqFAYN9Hp9IaGhscee6yvrw92LUjptVot7AUEQVy4cIFMJgP/​wuFwYDrFiD8vLw9zc3hf4Vx9+eWXb968iSugSCQaHh4OBoODg4M8Hs/​pdL744os/​+9nPUqnU8PDw4ODghg0bbty4ceDAgX/​84x9TU1N8Pr+oqKi+vh6ui+eff57L5f7xj3+EAjUej7e2ttbV1QFe/​3/​9GP/​rv/​7rhUzmRmMjPtKNjY2//​vWvuVzupUuXWltb/​2+f+fb29hs3bjQ2Nv7+97+HSjgej69Zs0YqlX700UdABuXk5OTn58PrRBDE4uIi3g74tvC7+Hw+FLQWiwWXWrfbTafTOzo6YBnBZQ57ezg8oEvmcrmgBAoEApvNtmnTJpFIBF5xKpV66aWXampqPB5PYWHh5s2be3t7kegplUqx/​sUcNX1vhYPfH93c9SerrUTKBEhhgUCgrq6ORCLxeDwKhSISiUwmE4fDweA4HA6PjIxotVoejwcGJNK3I5GIw+GARR4ULaCJJyYmMGosKCiYmpqCwxNzm/​379+NGj7KYTqdlMllxcTHYrYODg+g3oWMjk8lTU1PxeJzL5eK7RKfTFQpFXl7e/​Pw8pjQMBgPUQHwfcnNzMYXgcDhoaWtray0WSyKR4HA409PTRUVFYOKvW7dOIBCgekIRT6VSJRKJwWBYu3atWCxOJpNqtdpsNkOMiPrl8/​mYTCaHw5mcnMTKDvtneD5xHvj9/​rGxsVQqxWazEU7C5/​NZLBaSCxcXF9euXdvW1kYmk1977TW8HdA4NjY2JhIJiL6hSYfCj8Fg4ApCEASgxJmvswlLSkoCgYDJZGIymQRB0On0V199NZFI2Gw2gUCQ9UDEYrFz586p1WqVSvWXv/​yFTCarVCrwNbEwFAqFBEFkMpm33nrLYDDEYjFQoIVCIcCfiURidHQU6H9QcVwuVzwe1+v1OPv37dsHeIvT6dRoNA6HY+3atW+++aZEIhGJRFQqtaqq6sEHH7Tb7TQaDeMLMpk8PDycn5+v0+kikcjbb7/​d1tZ29uzZN95448SJE5i86fV6zGT0ej1BEK+88sovfvEL4JXw4L9iuFdZWYlfhkev12MCo1KpCIK4cOEChkg8Hu/​q1asDAwMEQZw+fRpsuO9///​tIiQKjJplMbt68GVR95He7XC7YyIG6w/​suFAqfeuopgiDwb9BzKJVKgUBQUVGRyWSgu4Xc8/​PPPy8oKIjFYlevXj1//​jw+//​iBR0ZGwD7DL8bnmSCImZmZ1dXVlpaWu10W/​j9+7nf0d/​fR6/​XZDhoqGqz1ZTLZ8PAwfg3glI2NjbixTkxMILwUZm7Ae2H1RCuEWlZaWmqz2WAngdg5kUigpKpUKtBL2Gw2k8lEqmo0GpVKpfF4HLB4EolEEITZbG5pabl9+zaa+uHhYbjwMRIBycDr9YrFYi6Xa7FY9uzZs2bNGthwsBYGah9Ql1gsdthsfpPB8Hg8CFzWarVLS0tQ1yAZjiAI0BzxEyJqnEwmoyEVCATYNLjdbpFIhJUDSC+xWEwikYDqLpPJenp6hoaGwP8xGAwPPPDAxMQEzkjkzYbDYZvNBoF2Tk4OAggxRkCpVSqVBoNBpVIhhQOtHAASeLlqa2upVOry8jIk23w+Xy6XT05OQkWzefPm783NXaqthdQP4zWUJLFY/​NJLL3388ccTExPwqZFIpGPHjg0MDACQibGSQqFoamqamZlB7l1XVxeTyYQMF5NlnJ3Z5Q2qGG4e0LaC1vD9739fo9Go1erCwsKBgQGv13vw4MF4PG6xWPr6+o4fP/​7HP/​5x79698ItBg48RB347evnBwcFYLNbT05NMJhsbG//​4xz8+99xzb7zxBp1O37dv3/​j4+EWCeNZiGRkZaWtru379Ou6IR48e7ejo6OnpuXHjBmT+Z86cuXnzps/​n6+/​vv3btWiqVOnLkyBdffFFdXe10OoPB4I4dO9rb29PpdEdHB3iTEL8i3X5lZeX555//​+OOP2Wx2ZWUlx5MDwQAAIABJREFU8lXodDqqMCq11WqVSCQYzqAHgvbX5/​OJxWI0/​lBAMRiMycnJ6elprVb75ZdfYitz5MiRX/​7yl5CxtrW1UalUjI+g9K2rqwuFQteuXbufMHX/​+R88GN3EYjGoCZFI6fP5IDtJp9OPPfbY7du3uVyu2WxeXV1dv3796uqqVquFJBzWefjCCwoKOBxObm6uUqkElguOcLhFoNgB1wny5NzcXMxeEAwN3Q6+UeFwGC4km81mt9uFQuHo6KjRaFSpVMlkksPhAIbl9/​u9Xi+bzUZLiHRDTJBBF4GaEJ3g/​Py8w+E4FQq9yWBAAw7hyuLiokwmE4vFbrcbd2dA8KH312g0WR8TJHTBYPC73/​0uvtsQQWIBSKFQqqqqIIFXKpVjY2MCgWB5eXlmZubBBx8EgUAoFHq93nA4DFcklUrds2cPg8EYHBxExczyIzOZjM/​nA30BvfDs7Cyiz8PhcEVFBYRA8/​PzeGFhooHaR6vVZjKZb3zjG40dHU1+fzQaraioQBUjCAKzMhQ7giAgDUJdQ/​PY0tICLrHX6+3v71epVKFQ6OrVq1QqFXSBeDx+6tQpBMgkk0ngt/​CCYGLDZDKdTud3vvOdjz76iMFgQKf//​vvvg6ng9Xp7eno2bNgwNTUFidTU1NTKysrhw4c3bdrU3t5OpVJPnjyJDFUEz+K6GQ6HscO/​cuVKKBTq6uqCaXZxcTGdTl/​IZE4EAhhP4X2n0WhICkS4OW5XXV1dMApgmU+lUkEzPnz48LVr1/​A6gMzMYDDQZJw7d+7atWunT59uaGi4du0aeKgikWhmZgauDszBsvFkkUiktLQUpAoKhdLS0tLR0QHDAdZOmUwG4cnwwcG2hvxCq9WKrDFAtuHwgEyosr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5fLLSoqam9vR5rG4/​GEQqHJZJqcnFy7di32JBjpeSnqmPhHPIIaBLJXh8MBMv7c3BzcPktLS3Nzcwkh1dXVoaGhTU1NIDVGRERg/​JXNZmN+BxwMLA1LgNIDlAujo6Pj4uIGBgYeeuih4eFhnU4XFBSEHBN53759+3Krq19Z+HmUQZBF4k2CX4QQptVq8UJsNjssLAynH0RY1BzQjLXZbIQQTKg+//​zzmzZtApWwpqampKTkwoULMzMzYLDQaDTY7aLuZLfbt27dOjAw4HQ69+/​f39raeunSpYaGhnvvvXf37t2ffvqp0+msqqrq6OjA8FRNTU1gYCB24unpaUTP5ubmpKQkjUaj0+m0Wm1QUJDBYNBqtb6+vqmpqY+PjZWvXz8wMGCz2QQCAQ6OsDYrLCxUKBTQMiopKbl48aLD4UhISAgODubz+ampqSkpKc3Nzdj/​8vPzc3Jy4HD7zDPPvPXWWzQabceOHdCHaGxshFgQHHElEgkG8XCCSUhIiIiIsNlsENKxWq1ZWVkymQx+ANgs0Y6urKx0uVwQnQ4KCjIajRibSE9P7+zshIhFe3u70Whcv349jUbr7u5GL1qhUHz++efV1dVkadXoqUB/​e1FWVubv74/​6A0hvycnJbrdbrVYHBARYLBYoRIL6xmazNRrN0NCQWCy22WwTExPZ2dkI0x6PB0VDMEzAiI+PjwcHHDZVqE76+fmhUoxOINwqULUAaQw8M1yEphi+RSC/​o6ogFApRvsB8CuZ14QQ0Nzc3Ojp67dq1qKgoEBZR7/​b19U1ISPhBe/​uTev3IyIhYLI6MjIRUukql8ng8cOGAJ1R5eTmPx+Pz+fn5+efOnfP394c4D0ylR0ZGPB7P6tWrh4aGQKDEq6BEDlcmk8m0Z88erVarVCqxUYGHt2nTprKyMnxRaQvuhoWFhZ988gkq/​jMzM1wu12azIaFDsuydoQXYbDY0hdRq9cTEBI1GW7lyZU9PD3J5gUDg4+Nz8eLFfyfkuL+/​VwPL6XSGhIQg8feWC44cOVJRUREREeEVc37qqafQDiGEQIUNZI+ioiIQ2IuKisrKyiIiIs6cOTM/​P5+bm7tx48bjx48XFxeLxWKFQuHxeCYmJoaHh1HJAdsEHzKbzQbJxOl0lpSUBAYGEkLWr1/​f39+/​b9++qqoqtC46OjpQx2MymRs3buzs7PTx8WEymQkJCSaTye12g3uK4h7mIZ4cH/​83lQp6dpj4Gx4eFgqFK1euxIy00WhkMpnl5eUlJSVXr14dGxsLDQ2FqlpFRQV6G/​Hx8SaT6YMPPmAymbOzs7W1tRCoMBgMHo/​HarWyWKy0tLTg4GCj0QgRDiaTGRcXl56ezuFwDAYDCEjY+DG+29zcjAIXqmeZmZm9vb2BgYE4KA8NDen1ejqdPjU1pdfrvaMJer0eVskKhUKlUiUkJGi12qqqKjzzsbGxZrN5KcVGqnRzewEVl7a2NvRjUQOBloBEIkHrD8xL6KjAIQ+6TllZWVA7kMvlkPpKTU2FKO74+LhWqw0NDTWZTMgNwb7QaDTQPdZqtXFxcUqlEp5wMGyan5/​HZkMIQc8Q2TEySow7SiSS0NBQiGWClKZSqQQCAUgRYrF4fHwcBf3c3Nzr16/​n5eVptVqpVAor6orLlx979NGxsbGoqChEnE2bNp08eRL7nI+PT2JiYnt7O3ajqakp0I1mZ2e1Wi0yPrDoUA0gC6KYdDod5JbZ2dmQkBCdTrdlyxZw+aHSvmHDhvLycu/​EI/​7z1VdfJYSgm4dcD+slhPzkJz85e/​YsCB6I9ZiWkkgk+HmJRCKRSORyOcR5NBrN008//​fvf/​95oNKLo4evra7XZWAu9WWTxmK31DvFCfh1lsRsrQqgjYeNBJR0zq2hLoM2A/​cNnwbkFk1Y5OTlnzpwB6xw57428b+9g59GjR8+cOYOnDq1sUHuPHj362muviUSi0dFRsVgMx0dCCIvFgri83W6/​++67u7u7w8PD0fDAWhgMhsPp9BcKoevw+OOPq1QqqVT6+eefQ4Ye+gFlZWXr16/​v7e1NSUnRarV4VlFLDAoKwqEnIiICliMgd4HAit2Rx+MFBga2trY6nU5Y68AsLDAwEPQeVKjQyNm3bx8hBAR5sjCCjk/​V5XJB8QYq86DSo/​uKNgkeBiRGhBBfX1+JRAIJ5draWjqdjomEpRQbKT36246tW7cicYNYislkgl4uJG0dDodWqzUajUlJSevWrbPb7Vu2bAkNDeVyuSqV6vz583q9HvLxyK2gKRgZGXnXXXetXr167dq1Lpdr2bJldDrdZrMhl/​R4PP7+/​nK53Et9Q2UGRVIw3CEoj6wWDDMUvv38/​CwWC5/​P9/​Hx8fX1nZycFIlEILpBtQbiAfHx8X19fagvQ+kQEuSEEMh4WSyW1NRUtOzi4uJQ/​yksLLx27RqaBEiTCSFJSUkqlSo6OvqBBx7wFjqGhobA0jMYDBBf0+l0Uqk0Pj4+JSVl27ZtFRUVzc3NYCIVFxfDrPHPjEBBLoK6AwbcCSFHjx718fG5cOEChH+9pkWjo6MwafH+346ODqvV+uCDDxqNxqNHjzY1NcE4F1IKCDQQVPH19d2/​f79AIJBIJGw2+8UXX4SDHSEELM+jR4+iMg77clTVMQeLqjpOAGKxGGUop9OZmJhICPEav5SWlubn53O53JiYGNCxYFUG7XVEZLwo0tiBgQE6nZ6fnw+1HIFAEB0d/​f7774eHh0M7AV1iLpcbGhoqk8lEIlFxcTHmvEwm05UrV+Lj4/​GJ+fn5wZMLDZLMzMyTJ0/​W1taeOnUKZh0ikWhqaqqrqysqKur69evx8fFcLlckEgUGBoaHh0dERAiFwoCAgK6urp6enuHhYY1G09vbu3nzZrgid3d3w3xmdHS0paUFJ7Pt27e3tLTA78VgMEAm/​rnnnsPklMPhKCsre/​311yFy4Ovry2azxWIx5khQEysrK8Pbk0qlL7/​8MrbYQ4cOHT16FKyqkpISVOHCwsIEAsH69etXr16dmZnpdrvPnz//​9wsQfxdQpZvbi7KystzcXKVS6Xa7cVREpcVms0GEFhKMvr6+Q0NDISEhIpEIgZLP50NpD5k+CCFcLhcGEUiI3G43aJc0Gg3awqmpqVAU8Xg8wcHB6K/​S6XRkPQidIFwiwLlcLhT3hUIhUic0Y1HDAdsHovmgDBqNRkw/​0Wi02NjY1NRUFoul1+sDAgKQKD3Q0/​N2aCjOHGC2ffbZZxaLxcfHRyaTnTlzBmNH9AW/​2dnZ2dWrVysUioCAgJGREajawhkRXTu3263VapEVWiyW7du3j42N1dbWbty40Ww2P/​fccw0NDQ0NDQaDASchCBOWlpbW1tY++eSTn3/​++c9+9rMbKzl1dXUYp0I7xOsz7vF4sFNimCAwMBBja7D4qK2tRdkKMsUCgaC1tbVwZuZlFgs0ldzc3KamJmyimKF1u90lJSVVVVUQEjhy5MiVK1csFgvyRH9/​fwR07N9Ib91u94EDB8AKxWmDEHLo0KGqqqrc3NyysjKFQmG1Wm02G5/​Pj4mJUavVPj4+GOWHWCl063bt2uVwOKKjo999911I6qPcgcfAYDDAaywiImJ6etpoNIrF4mXLlsEO8I477li1ahUMCSCILZVK77777q21tXNFRQaDASNyu3fvdjgc9fX1PB4Pdwp5NCFkYGCAELJnzx4ejxccHMxkMtva2hQKxfj4uFAo1Gq1MzMzzzzzzPHjx1kslkKhwBiaWCx2u93T09Pbtm0LCAiIjY2tr68HrQgtdKfTee7cOTy0PB7vwIEDFy9evHLlCmbH4ACD1jfmddHMHxwcdLvd6Je43e6qqqqqqioIXzc3NyPc19TUsFisCxcubN269dNPPw0JCcErLqXYSAX624uysjKdThcYGDg6OspmsyFbhvq42+3GlxlDQLD10ev1mF9FrRDRE4PjIKWgQtLb2wvTBhBm3G53f38/​KrN2ux3fCrARUP1HCiORSAQCAWrxaEhOTU1FR0djWgrj5qgShISEoK9Ao9HMZjOHwyGE4K/​hONLa2moymSCXiCGUkJCQubm5H3V3f5GWNjIyQhZ0g6VSaWRk5JYtW9hstr+/​f3p6+po1a8xmM/​YnOp0+NDQEYxP0gSMiIqCFYDQaoSuAKGy1WnNzcz/​55BMoaGLktb6+Pjk5WaPRoCAeEhICjjzkLSsrK/​HdRnRwuVwIASjco0HidDrFYjGqZ+hPQMcc9wWeKkhsN2/​eDF4jZiwLCgpyq6uPLKgBX758GT+MwSi8Z4Ts0tJSiG6iaofwbbfbERyRaWJueXZ2tqKiAuS/​o0ePQmby2LFjAoEgKysL1/​GBwLUKtwy6zampqdXV1c8880x5efnAwIDJZOrt7fXx8SkuLq6oqMDrFhUVWa1WoVAoEokYDAbMUrKzsyUSSV1d3SOPPIIWUU1NjVgsXr16NU6c6enpLpdrR3Pzm8HBOp2OyWQODQ11dnaCcT87O2uz2Z5++umzZ8/​C6IbP56ekpLz11lsBAQFnzpxRKpVgIoCCJRaLn3vuuTfffBN3H+pGbrc7LCwMJCuFQjE8PLxx48bGxsb77rtPLBarVKqxsTEoTKCFPjU11dPTA5IMNItwf1EIAofS399fo9GgA49Xx0Qb/​o5XRg2Ph0ajEYvFUDN+8skn3333XUI1YyncPMrKylwul0KhwFA1eAgOh6Ovr08qlc7OznoHNCBnBj7AY4891tvbm5GRoVQqY2NjwaWZmZlBISUgIIDH46Wnp+Oojl1hYmIiNzcX6ZVQKAwJCVGpVHiyEdlRKEDPyul0gnvjdDqhdQUFG3gnYXQFtXtfX19v/​cdoNAYGBk5MTEgkkuTk5NjYWLSFbTYbdiOHw/​Gj7u53o6LodDqDwUBdFYZKKpWKxWJ1dHRER0fD1nXjxo1TU1M4vniVA+Li4uRy+ezsLHgUmKdH6A8I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en/learning/schools/s01/code-examples/ba-ce-10-01.txt ยท Last modified: 2015/09/22 16:21 (external edit)