{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data classification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data classification is a common task in geospatial data analysis that determines the assignment of values to distinct classes.\n", "Classifying original values into categories may help simplify the data for further analysis or communicating the results. Data classification is central when visualizing geographic information to correctly represent the distribution of the data. \n", "\n", "Here, we will get familiar with classification schemes from the [PySAL](https://pysal.org/) [^pysal] [`mapclassify` library](https://pysal.org/mapclassify/) [^mapclassify] that is intended to be used when visualizing thematic maps. Further details of geographic data visualization will be covered in chapter 8. We will also learn how to classify data values based on pre-defined threshold values and conditional statements directly in `geopandas`. \n", "\n", "Our sample data is an extract from the Helsinki Region Travel Time Matrix ({cite}`Tenkanen2020`) that represents travel times to the central railway station across 250 m x 250 m statistical grid squares covering the Helsinki region. Let's read in the data and check the first rows of data: " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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car_m_dcar_m_tcar_r_dcar_r_tfrom_idpt_m_dpt_m_tpt_m_ttpt_r_dpt_r_tpt_r_ttto_idwalk_dwalk_tgeometry
03229743322604857856403261611614732616108139597537532164459POLYGON ((382000 6697750, 381750 6697750, 3817...
13250843324714957856413282211914532822111133597537529547422POLYGON ((382250 6697750, 382000 6697750, 3820...
23013350318725657856423294012114632940113133597537529626423POLYGON ((382500 6697750, 382250 6697750, 3822...
33269054344296057856433323312515033233117144597537529919427POLYGON ((382750 6697750, 382500 6697750, 3825...
43187242318344857875443212710912632127101121597537531674452POLYGON ((381250 6697500, 381000 6697500, 3810...
\n", "
" ], "text/plain": [ " car_m_d car_m_t car_r_d car_r_t from_id pt_m_d pt_m_t pt_m_tt \\\n", "0 32297 43 32260 48 5785640 32616 116 147 \n", "1 32508 43 32471 49 5785641 32822 119 145 \n", "2 30133 50 31872 56 5785642 32940 121 146 \n", "3 32690 54 34429 60 5785643 33233 125 150 \n", "4 31872 42 31834 48 5787544 32127 109 126 \n", "\n", " pt_r_d pt_r_t pt_r_tt to_id walk_d walk_t \\\n", "0 32616 108 139 5975375 32164 459 \n", "1 32822 111 133 5975375 29547 422 \n", "2 32940 113 133 5975375 29626 423 \n", "3 33233 117 144 5975375 29919 427 \n", "4 32127 101 121 5975375 31674 452 \n", "\n", " geometry \n", "0 POLYGON ((382000 6697750, 381750 6697750, 3817... \n", "1 POLYGON ((382250 6697750, 382000 6697750, 3820... \n", "2 POLYGON ((382500 6697750, 382250 6697750, 3822... \n", "3 POLYGON ((382750 6697750, 382500 6697750, 3825... \n", "4 POLYGON ((381250 6697500, 381000 6697500, 3810... " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pathlib import Path\n", "import numpy as np\n", "import geopandas as gpd\n", "import matplotlib.pyplot as plt\n", "\n", "data_dir = Path(\"data\")\n", "grid_fp = data_dir / \"Helsinki\" / \"TravelTimes_to_5975375_RailwayStation.shp\"\n", "\n", "# Read files\n", "grid = gpd.read_file(grid_fp)\n", "grid.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Detailed column descriptions are available in Table 3, {cite}`Tenkanen2020`. We will use column `'pt_r_t'` which contains information about travel time in minutes to the central railway station by public transportation in rush hour traffic. Missing data are presented with value -1. Let's set the missing values as `NaN` to exclude no data from further analysis:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "grid = grid.replace(-1, np.nan)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classification schemes\n", "\n", "We will now learn how to use `mapclassify`to assing the data vaules into distinct classes. `Mapclassify` allows applying various classification schemes on our data that partition the attribute values into mutually exclusive groups. Choosing an adequate classification scheme and number of classes depends on the message we want to convey with our map and the underlying distribution of the data. Available classification schemes include: \n", "\n", "- box_plot\n", "- equal_interval\n", "- fisher_jenks\n", "- fisher_jenks_sampled\n", "- headtail_breaks\n", "- jenks_caspall\n", "- jenks_caspall_forced\n", "- jenks_caspall_sampled\n", "- max_p_classifier\n", "- maximum_breaks\n", "- natural_breaks\n", "- quantiles\n", "- percentiles\n", "- std_mean\n", "- user_defined\n", " \n", "See {cite}`Rey_et_al_2023` for a thorough introduction on the mathematics behind each classification scheme. These classification schemes can be used directly when plotting data in `geopandas` as long as `mapclassify` package is also installed.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Choosing a classification scheme\n", "\n", "Let's have a look at the distribution of the public transport travel times through checking the histogram and descriptive statistics. A histogram is a graphic representation of the distribution of the data. Descriptive statistics summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding `NaN` values. While looking at the histogram, remember that each observation is one 250 meter x 250 meter grid square in the Helsinki region and the histogram shows the distribution of travel times to the central railway station across the whole region. \n", "\n", "For exploring the different classification schemes, let's create a `pandas` `Series` without `NaN` values." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "lines_to_next_cell": 0 }, "outputs": [], "source": [ "# Creating a data Series withouth NaN values\n", "travel_times = grid.loc[grid[\"pt_r_t\"].notnull(), \"pt_r_t\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot a histogram\n", "travel_times.plot.hist(bins=50, color=\"lightgray\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_**Figure 6.60**. Histogram of the travel time values. Data source: Tenkanen & Toivonen 2020._" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 13020.000000\n", "mean 53.124654\n", "std 21.295944\n", "min 0.000000\n", "25% 38.000000\n", "50% 49.000000\n", "75% 65.000000\n", "max 181.000000\n", "Name: pt_r_t, dtype: float64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "travel_times.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The maximum travel time to the central railway station by public transport (including time for walking) is 181 minutes, i.e. over three hours. Most of the travel times range between 38 and 65 minutes with an average travel time of 53 minutes. Looking at the histogram (Figure 8.6), we can tell than only a handful of grid squares have more than two hour travel times to the central railway station. These grid squares are most likely located in rather inaccessible places in terms of public transport accessibility. \n", "\n", "Let's have a closer look at how these `mapclassify` classifiers work and try out different classification schemes for visualizing the public transport traveltimes. In the interactive version of this book, you can try out different numbers of classes and different classification schemes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Natural breaks\n", "\n", "First, let's try out natural breaks classifier that tries to split the values into natural clusters. The number of observations per bin may vary according to the distribution of the data." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "NaturalBreaks\n", "\n", " Interval Count\n", "------------------------\n", "[ 0.00, 24.00] | 604\n", "( 24.00, 34.00] | 1689\n", "( 34.00, 42.00] | 2360\n", "( 42.00, 49.00] | 1885\n", "( 49.00, 58.00] | 1978\n", "( 58.00, 68.00] | 1718\n", "( 68.00, 80.00] | 1412\n", "( 80.00, 94.00] | 756\n", "( 94.00, 113.00] | 456\n", "(113.00, 181.00] | 162" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import mapclassify\n", "\n", "mapclassify.NaturalBreaks(y=travel_times, k=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's possible to extract the threshold values into an array:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([ 22., 33., 40., 48., 57., 66., 76., 90., 109., 181.])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.NaturalBreaks(y=travel_times, k=10).bins" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can further explore the classification on top of the histogram:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# Define classifier\n", "classifier = mapclassify.NaturalBreaks(y=travel_times, k=10)\n", "\n", "# Plot histogram for public transport rush hour travel time\n", "grid[\"pt_r_t\"].plot.hist(bins=50, color=\"lightgray\", title=\"Natural Breaks\")\n", "\n", "# Add vertical lines for class breaks\n", "for break_point in classifier.bins:\n", " plt.axvline(break_point, linestyle=\"dashed\", linewidth=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_**Figure 6.61**. Histogram of the travel time values with natural breaks classification into 10 groups. Data source: Tenkanen & Toivonen 2020._" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can apply the classifier on our data and store the result in a new column." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 8\n", "1 8\n", "2 8\n", "3 9\n", "4 8\n", "Name: pt_r_t_nb, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Classify the data\n", "grid[\"pt_r_t_nb\"] = grid[[\"pt_r_t\"]].apply(classifier)\n", "grid[\"pt_r_t_nb\"].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can visualize our data using the classification scheme when plotting the data in `geopandas` through adding the `scheme` option, while the parameter `k` defines the number of classess to use. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the data using natural breaks\n", "ax = grid.plot(\n", " figsize=(6, 4),\n", " column=\"pt_r_t\",\n", " linewidth=0,\n", " scheme=\"natural_breaks\",\n", " k=10,\n", ")\n", "\n", "# Set the x and y axis off and adjust padding around the subplot\n", "plt.axis(\"off\")\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_**Figure 6.62**. Travel times visualized using the natural breaks classification scheme. Data source: Tenkanen & Toivonen 2020._" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Quantiles \n", "\n", "Next, let's explore the quantiles classification that splits the data so that each class has an equal number of observations. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "Quantiles\n", "\n", " Interval Count\n", "------------------------\n", "[ 0.00, 30.00] | 1406\n", "( 30.00, 36.00] | 1436\n", "( 36.00, 40.00] | 1242\n", "( 40.00, 44.00] | 1135\n", "( 44.00, 49.00] | 1319\n", "( 49.00, 55.00] | 1392\n", "( 55.00, 62.00] | 1342\n", "( 62.00, 70.00] | 1265\n", "( 70.00, 81.00] | 1190\n", "( 81.00, 181.00] | 1293" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.Quantiles(y=travel_times, k=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the numerical range of the groups created using the quantiles classification scheme may vary greatly depending on the distribution of the data. In our example, some classes have more than 30 min interval, while others less than 10 minutes. The default number of classes is five (quintiles), but you can set the desired number of classes using the `k` parameter. In the interactive version of the book, you can try changing the number of classes and see what happens to the class intervals; more classes get added around the central peak of the histogram if increasing the number of classes." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# Define classifier\n", "classifier = mapclassify.Quantiles(y=travel_times, k=10)\n", "\n", "# Plot histogram for public transport rush hour travel time\n", "grid[\"pt_r_t\"].plot.hist(bins=50, color=\"lightgray\", title=\"Quantiles\")\n", "\n", "# Add vertical lines for class breaks\n", "for break_point in classifier.bins:\n", " plt.axvline(break_point, linestyle=\"dashed\", linewidth=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_**Figure 6.63**. Histogram of the travel time values with Quantile classification into 10 groups. Data source: Tenkanen & Toivonen 2020._\n", "\n", "If comparing the histograms of natural breaks and quantile classifications, we can observe that natural breaks might work better to display differences in the data values across the whole data range, while quantiles would help distinguishing differences around the central peak of the data distribution. However, neither of the classification schemes display differences in short, less than 25 minute travel times which might be important for making an informative map. Also, we might want to have round numbers for our class values to facilitate quick and intuitive interpretation. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "lines_to_next_cell": 2 }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the data using quantiles\n", "ax = grid.plot(\n", " figsize=(6, 4),\n", " column=\"pt_r_t\",\n", " linewidth=0,\n", " scheme=\"quantiles\",\n", " k=10,\n", " legend=True,\n", " legend_kwds={\"title\": \"Travel times (min)\", \"bbox_to_anchor\": (1.4, 1)},\n", ")\n", "\n", "# Set the x and y axis off and adjust padding around the subplot\n", "plt.axis(\"off\")\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_**Figure 6.64**. Static map of travel times visualized using the quantiles classification scheme. Data source: Tenkanen & Toivonen 2020._" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 9\n", "1 9\n", "2 9\n", "3 9\n", "4 9\n", "Name: pt_r_t_q10, dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Store the class index numbers\n", "grid[\"pt_r_t_q10\"] = grid[[\"pt_r_t\"]].apply(classifier)\n", "grid[\"pt_r_t_q10\"].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The quantile classification allows us to extract, for example the best 10 % of all grid squares in terms of travel times to the central railway station. Now that we divided the data into quintiles, we can get the top 10 % of the data through extracting the first category of our classified values. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid[grid[\"pt_r_t_q10\"] == 0].explore()" ] }, { "cell_type": "raw", "metadata": { "editable": true, "raw_mimetype": "", "slideshow": { "slide_type": "" }, "tags": [ "hide-cell" ] }, "source": [ "% This cell is only needed to produce a figure for display in the hard copy of the book.\n", "\\adjustimage{max size={0.9\\linewidth}{0.9\\paperheight}, caption={\\emph{\\textbf{Figure 6.65}. Top 10 % out of all statistical grid squares in the Helsinki Region in terms of public transport travel times to the Helsinki.}}, center, nofloat}{../img/figure_6-65.png}\n", "{ \\hspace*{\\fill} \\\\}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_**Figure 6.65**. Top 10 % out of all statistical grid squares in the Helsinki Region in terms of public transport travel times to the Helsinki. Data source: Tenkanen & Toivonen 2020._" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Pretty breaks\n", "\n", "The pretty breaks classification shceme rounds the class break values and divides the range equally to create intervals that look nice and that are easy to read. This classification scheme might be tempting to use as it creates intuitive and visually appealing intervals. However, depending on the distribution of the data, the group sizes might vary greatly which might lead to misleading visualizations." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pretty\n", "\n", " Interval Count\n", "------------------------\n", "[ 0.00, 20.00] | 335\n", "( 20.00, 40.00] | 3749\n", "( 40.00, 60.00] | 4822\n", "( 60.00, 80.00] | 2740\n", "( 80.00, 100.00] | 933\n", "(100.00, 120.00] | 351\n", "(120.00, 140.00] | 84\n", "(140.00, 160.00] | 0\n", "(160.00, 180.00] | 5\n", "(180.00, 200.00] | 1" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapclassify.PrettyBreaks(y=travel_times, k=10)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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UOum6ILpQDB07dgxbtmxBfHy83/NpaWkwGo3IysryLSspKUFOTg4GDRoEABg4cCBsNhv27t3rG/P111/DZrP5xhAREVFo0/SkarvdjuPHj/u+zsvLw8GDBxEXF4fk5GTcd999yM7OxieffAKPx+M75ycuLg4mkwkWiwWTJ0/GzJkzER8fj7i4OMyaNQupqakYNmwYAODqq6/GHXfcgSlTpmDFihUAgEcffRQZGRmXPKGaiIiIQoumBdE333yDIUOG+L5+6qmnAACTJk3CvHnz8M9//hMAcO211/q9btu2bRg8eDAAYMmSJYiIiMDYsWNRW1uLoUOHYvXq1QgPD/eNf//99/Hkk0/6utFGjRrV4LWPKDAJbcz4/eAeSGhj1jqKKu2iwnFf77ZoFxXe+GCdkHFbM7MYzCwGM4uhh8wGRVEUzX66RCorK2GxWGCz2Xg+kaRycnIu+7we2+6JiKhpAn3/1vU5RKRPdocbu38sg90hT/s6ANS4vDhkrUONS56bHsq4rZlZDGYWg5nF0ENmFkSk2smz1bj/r3tw8my11lFUKa504b+2nEZxpTytqDJua2YWg5nFYGYx9JCZBRERERGFPBZEREREFPJYEBEREVHIY0FEqkWEG5DUNhIR4Ze+9YkeRYQZEB8djogweXLLuK2ZWQxmFoOZxdBDZrbdB4ht9/Jj2z0RUehh2z0RERFRgFgQkWo/WCtx48J/4wdrpdZRVDlZ7sSkDYU4We7UOkrAZNzWzCwGM4vBzGLoITMLIlLN7VFgrayD2yPXp61ur4KyGg/cXnlyy7itmVkMZhaDmcXQQ2YWRERERBTyWBARERFRyGNBRERERCGPbfcBYtv9T+wONw4X2pDa2YI25git4wRs74FDOF7mxC/jTYg21v9bQI9t9zJua2YWg5nFYGYxWjJzoO/fLIgCxIJIfrwOERFR6OF1iKjFWG11eGXTD7Da6rSOosrZGjdWHyjH2Rq31lECJuO2ZmYxmFkMZhZDD5lZEJFqZ+0OLP/yR5y1O7SOokpFrQf/yK1ERa1H6ygBk3FbM7MYzCwGM4uhh8wsiIiIiCjksSAiIiKikMeCiIiIiEIeCyJSrV20EeP6d0G7aKPWUVRpaw5Heo82aGsO1zpKwGTc1swsBjOLwcxi6CEz2+4DxLZ7+bHtnogo9LDtnlpMncuDo6erUOeSp1sLABxuL/IrnHC4vVpHCZiM25qZxWBmMZhZDD1kZkFEqh0vtSN9yVc4XmrXOooqBTYXHv+kBAU2l9ZRAibjtmZmMZhZDGYWQw+Z5bimN5EA/EiNiCh08QgRERERhTwWRERERBTyWBBRUEzh8k0dg8GAiLDz/5WJjNuamcVgZjGYWQytM7PtPkBsu5dfY+cINYbnEBERyYdt90REREQBYkFEqh0vrcLIP+/A8dIqraOoUmBzYcansrXdy7etmVkMZhaDmcXQQ2YWRKRancuL3OJK1LnkucAhcP7CjD+Wy3ZhRvm2NTOLwcxiMLMYesjMgoiIiIhCHgsiIiIiCnksiIiIiCjkse0+QGy7/4mtxoWdx8/i5l8mwBJt1DpOwPbs/xYHrXW4NikSbczhql+vRdu9jNuamcVgZjGYWYyWzBzo+zcLogCxIJIfr0NERBR6eB0iajFnqhz4244TOFPl0DqKKuW1Hmz8rhLltR6towRMxm3NzGIwsxjMLIYeMrMgItVOV9Zhwaff43RlndZRVCmrcWNldjnKatxaRwmYjNuamcVgZjGYWQw9ZGZBRERERCGPBRERERGFPBZEREREFPJYEJFqsZERGHZ1R8RGRmgdRZUYUxhu+EUUYkzyTHsZtzUzi8HMYjCzGHrIzLb7ALHtXn5suyciCj1su6cW4/J4UWZ3wOWR58aBAOD2KrDVeeD2yvM3gIzbmpnFYGYxmFkMPWRmQUSqHbFWIW3BFhyxVmkdRZWT5U488I9CnCx3ah0lYDJua2YWg5nFYGYx9JCZBRERERGFPBZEREREFPI0LYi++uor3HXXXUhOTobBYMBHH33k97yiKJg3bx6Sk5MRFRWFwYMHIzc312+Mw+HA9OnTkZCQgJiYGIwaNQqFhYV+Y8rLyzFx4kRYLBZYLBZMnDgRFRUVLbx2REREJAtNC6Lq6mr07dsXS5cubfD5V199FYsXL8bSpUuxb98+JCUlYfjw4aiq+ukzxszMTGzcuBHr16/Hzp07YbfbkZGRAY/np/tVTZgwAQcPHsSmTZuwadMmHDx4EBMnTmzx9aPmlZOTc9kHERFRsHTTdm8wGLBx40bcfffdAM4fHUpOTkZmZiaeeeYZAOePBiUmJuKVV17B1KlTYbPZ0KFDB7z33nsYN24cAKC4uBhdunTBZ599httvvx3ff/89rrnmGuzZswcDBgwAAOzZswcDBw7EDz/8gCuvvDKgfGy7/4nHq6DG6Ua0KQLhYQZhP7exoqextvhvDx2Gw63AHGEIKrcWbfdabeumYGYxmFkMZhajJTNL33afl5cHq9WK9PR03zKz2Yxbb70Vu3btAgDs378fLpfLb0xycjL69OnjG7N7925YLBZfMQQAN954IywWi29MQxwOByorK/0edF54mAGxkUZpftEuCA8zINoUJlVuGbc1M4vBzGIwsxh6yKzbgshqtQIAEhMT/ZYnJib6nrNarTCZTGjfvv1lx3Ts2LHe9+/YsaNvTEMWLVrkO+fIYrGgS5cuTVqf1iTvbDUmrvwaeWertY6iSlGlC8//+zSKKl1aRwmYjNuamcVgZjGYWQw9ZNZtQXSBweBfLSqKUm/ZxS4e09D4xr7P7NmzYbPZfI+CggKVyVuvaocbO46dRbXDrXUUVWpdXhwoqUOtS56Llcm4rZlZDGYWg5nF0ENm3RZESUlJAFDvKE5paanvqFFSUhKcTifKy8svO+b06dP1vv+ZM2fqHX36ObPZjLZt2/o9iIiIqHXSbUGUkpKCpKQkZGVl+ZY5nU5s374dgwYNAgCkpaXBaDT6jSkpKUFOTo5vzMCBA2Gz2bB3717fmK+//ho2m803hoiIiEKbprfCtdvtOH78uO/rvLw8HDx4EHFxcejatSsyMzOxcOFC9OzZEz179sTChQsRHR2NCRMmAAAsFgsmT56MmTNnIj4+HnFxcZg1axZSU1MxbNgwAMDVV1+NO+64A1OmTMGKFSsAAI8++igyMjIC7jAjIiKi1k3Tguibb77BkCFDfF8/9dRTAIBJkyZh9erVePrpp1FbW4tp06ahvLwcAwYMwObNmxEbG+t7zZIlSxAREYGxY8eitrYWQ4cOxerVqxEeHu4b8/777+PJJ5/0daONGjXqktc+osZ1skTixdG90ckSqXUUVTrEROCx6+PQIUbTaa+KjNuamcVgZjGYWQw9ZNbNdYj0jtch0l5Tr0PU1Is3anEdIiIiahrpr0NE+lVR48TGA4WoqJHnrvEAUOXwYNsJO6ocnsYH64SM25qZxWBmMZhZDD1kZkFEqhWW1+IPH36LwvJaraOoctruxhu7ynDaLk8rqozbmpnFYGYxmFkMPWRmQUREREQhjwURERERhTwWRERERBTyWBCRalGmcPTr2g5RpvDGB+tIZEQYrkwwITJCnmkv47ZmZjGYWQxmFkMPmdl2HyC23WuPbfdERKQW2+6JiIiIAsSCiFTLKbKh+7OfIqfIpnUUVY6XOZCxNh/HyxxaRwmYjNuamcVgZjGYWQw9ZGZBRERERCGPBRERERGFPBZEREREFPJYEBEREVHIY9t9gNh2/5M6lwdWWx2SLJGINIq7ZkRT2+6zvz2MszVuJERHwBRuUP3ztWi712pbNwUzi8HMYjCzGC2ZOdD374hm/akUEiKN4eieEKN1DNVM4QYkxxq1jqGKjNuamcVgZjGYWQw9ZOZHZqRawbkaZK4/gIJzNVpHUcVqd+H1nWdhtbu0jhIwGbc1M4vBzGIwsxh6yMyCiFSz1brw0cFi2GrlKSwAwO7w4suT1bA7vFpHCZiM25qZxWBmMZhZDD1kZkFEREREIY/nEBE1o6ae+E1ERNrgESIiIiIKeSyISLWOsWbMGNoTHWPNWkdRJS4qHPenWhAXJUcbKiDntmZmMZhZDGYWQw+ZeR2iAPE6RNpr6sdRjb2+MYF83MWPzIiI9CXQ928eISLVqupc2H70DKrq5OlgAIAapxf7i2tR45Sny0zGbc3MYjCzGMwshh4ysyAi1fLLajDpnb3IL5PnGhcAUFzlwtytpSiukucfCRm3NTOLwcxiMLMYesjMLjNqNZr6kRgREYUuHiEiIiKikMeCiIiIiEIeCyJSzRQRhm7x0TBFyDV9jOEGdGoTAWMQd7rXiozbmpnFYGYxmFkMPWRm232A2HavPa3PEWLbPRGRfNh2T0RERBQgFkSk2vcllbjupSx8X1KpdRRV8sqdmPD3AuSVO7WOEjAZtzUzi8HMYjCzGHrIzIKIVPN4FZyrdsLjlevTVo9XQaXDK1VuGbc1M4vBzGIwsxh6yMyCiIiIiEIeCyIiIiIKeSyIiIiIKOSx7T5AbLv/SbXDjR+sVbgqKRYxZnF3f2lq232ty4uTFS50b2dElFH93wJatN1rta2bgpnFYGYxmFmMlswc6Ps3C6IAsSDSHq9DREREavE6RNRiSmy1eOmT71Biq9U6iipnq9346zfncLbarXWUgMm4rZlZDGYWg5nF0ENmFkSkWpndiZU781Bml+d6PgBQUefBxz9UoaLOo3WUgMm4rZlZDGYWg5nF0ENmFkREREQU8lgQERERUchjQUREREQhjwURqdY+xoSJN3ZD+xiT1lFUaRsZjpG92qBtZLjWUQIm47ZmZjGYWQxmFkMPmdl2HyC23WuPbfdERKQW2+6pxdQ6PcgpsqHWKU+3FgDUub04XuZAndurdZSAybitmVkMZhaDmcXQQ2YWRKTaj2fsyHhrJ348Y9c6iiqFNhcyP7ei0ObSOkrAZNzWzCwGM4vBzGLoITMLIiIiIgp5ui6I3G43nnvuOaSkpCAqKgpXXHEFXnzxRXi9P33koSgK5s2bh+TkZERFRWHw4MHIzc31+z4OhwPTp09HQkICYmJiMGrUKBQWFopeHSIiItIpXRdEr7zyCt5++20sXboU33//PV599VW89tpreOutt3xjXn31VSxevBhLly7Fvn37kJSUhOHDh6Oqqso3JjMzExs3bsT69euxc+dO2O12ZGRkwOOR5/NVIiIiajm6vg3u7t27MXr0aIwcORIA0L17d6xbtw7ffPMNgPNHh958803MmTMHY8aMAQCsWbMGiYmJ+OCDDzB16lTYbDasXLkS7733HoYNGwYAWLt2Lbp06YItW7bg9ttv12blJGYwAG3METAYtE6iTpjBgCijAWESBZdxWzOzGMwsBjOLoYfMum67f/nll/H2229j8+bN6NWrF7799lukp6fjzTffxP33348TJ06gR48eyM7ORr9+/XyvGz16NNq1a4c1a9Zg69atGDp0KM6dO4f27dv7xvTt2xd333035s+f3+DPdjgccDgcvq8rKyvRpUsXtt1riG33RESkVqBt97o+QvTMM8/AZrPhqquuQnh4ODweD/70pz/h/vvvBwBYrVYAQGJiot/rEhMTkZ+f7xtjMpn8iqELYy68viGLFi26ZLFERERErYuuzyH68MMPsXbtWnzwwQfIzs7GmjVr8Prrr2PNmjV+4wwXHWNTFKXesos1Nmb27Nmw2Wy+R0FBQfAr0socO12F4Yu349jpqsYH68ipCiem/asYpyrkuQO0jNuamcVgZjGYWQw9ZNb1EaI//vGPePbZZzF+/HgAQGpqKvLz87Fo0SJMmjQJSUlJAM4fBerUqZPvdaWlpb6jRklJSXA6nSgvL/c7SlRaWopBgwZd8mebzWaYzeaWWC3pOdxeHCu1wyHRBQ4BwOlRcMrmgtOj20+J65FxWzOzGMwsBjOLoYfMuj5CVFNTg7Aw/4jh4eG+tvuUlBQkJSUhKyvL97zT6cT27dt9xU5aWhqMRqPfmJKSEuTk5Fy2ICIiIqLQoesjRHfddRf+9Kc/oWvXrujduzcOHDiAxYsX45FHHgFw/qOyzMxMLFy4ED179kTPnj2xcOFCREdHY8KECQAAi8WCyZMnY+bMmYiPj0dcXBxmzZqF1NRUX9cZERERhTZdF0RvvfUWnn/+eUybNg2lpaVITk7G1KlT8cILL/jGPP3006itrcW0adNQXl6OAQMGYPPmzYiNjfWNWbJkCSIiIjB27FjU1tZi6NChWL16NcLD5bnrOREREbUcXbfd6wnvdv8TW60Le/PO4YaUOFiijMJ+blPb7u1OL3JP16F3YiTamNR/WqxF271W27opmFkMZhaDmcVoycyBvn+zIAoQCyLt8TpERESkVqu4DhHpU2lVHf7+TSF+078zOsZGNtv3bemCp7zWg6wf7Rjeow3aR+nz49KLt8HFmWUoqFpqfrQkZhaDmcVg5uDousuM9Km00oHXvjiC0kpH44N1pKzGjXcPVqCsxq11lIDJmFnG+cHMYjCzGMwcHBZEREREFPJYEBEREVHIY0FEREREIY8FEanWNtKIO1OT0DZSjnbOC9qYwnBT1+igWu61ImNmGecHM4vBzGIwc3DYdh8gtt23PK3b6hsjou2ebftERM0r0Pdvef7sJN1wur0osdXCKdGNAwHA5VFwttoNl0Q3d5Uxs4zzg5nFYGYxmDk4LIhItaOnqzBw0VYcPV2ldRRV8iuceGhjEfIrnEG9Picnp9FHc2tqZi3IOD+YWQxmFoOZgxNUQZSXl9fcOYiIiIg0E1RB9Mtf/hJDhgzB2rVrUVdX19yZiIiIiIQKqiD69ttv0a9fP8ycORNJSUmYOnUq9u7d29zZiIiIiIQIqiDq06cPFi9ejKKiIqxatQpWqxU333wzevfujcWLF+PMmTPNnZOIiIioxTRL273D4cCyZcswe/ZsOJ1OGI1GjBs3Dq+88go6derUHDk1x7b7n3i9ClxeL4xhYQgLMzTb923ptnuvosDjBcLDgDBD8+VWQ23b/cWZZWi7b6n50ZKYWQxmFoOZ/Qlpu//mm28wbdo0dOrUCYsXL8asWbPw448/YuvWrSgqKsLo0aOb8u1Jp8LCDDBHhEvzi3ZBmMEAY7hBs2IoGFJmlnB+MLMYzCwGMweZIZgXLV68GKmpqRg0aBCKi4vx7rvvIj8/HwsWLEBKSgpuuukmrFixAtnZ2c2dl3TgxBk7xq3YjRNn7FpHUaWo0oVnN1tRVOnSOkrAZMws4/xgZjGYWQxmDk5EMC9avnw5HnnkETz88MNISkpqcEzXrl2xcuXKJoUjfapxevB13jnUOD1aR1Gl1uVFTqkDtS55LlYmY2YZ5wczi8HMYjBzcIIqiI4dO9boGJPJhEmTJgXz7YmIiIiECuojs1WrVuHvf/97veV///vfsWbNmiaHIiIiIhIpqILo5ZdfRkJCQr3lHTt2xMKFC5scioiIiEikoAqi/Px8pKSk1FverVs3nDp1qsmhSN+S20Xh5TGpSG4XpXUUVTrERGD6gDh0iAnqk2JNyJhZxvnBzGIwsxjMHJygrkPUtWtXLF26FKNGjfJb/vHHH+Pxxx9HYWFhswXUC16HqOW19HWI9EDtdYjUvp6IiPy16HWIxo8fjyeffBLbtm2Dx+OBx+PB1q1bMWPGDIwfPz7o0CSHc9VOrN97Cueq5bkDOwDY6jz44lgVbHXydF7ImFnG+cHMYjCzGMwcnKAKogULFmDAgAEYOnQooqKiEBUVhfT0dNx22208hygEFFfU4tkNh1FcUat1FFXOVLvx1tfncKbarXWUgMmYWcb5wcxiMLMYzBycoE5MMJlM+PDDD/HSSy/h22+/RVRUFFJTU9GtW7fmzkdERETU4pp0pmavXr3Qq1ev5spCREREpImgCiKPx4PVq1fj3//+N0pLS+H1+l9Fd+vWrc0Sjoj88aRrIqKWEVRBNGPGDKxevRojR45Enz59YJDoxpPUdNGmcAxIiUO0KVzrKKpEGcPQp6MZUcYm3dNYKBkzyzg/mFkMZhaDmYMTVNt9QkIC3n33Xdx5550tkUmX2Hbf8th23/RtwCNERET+WrTt3mQy4Ze//GXQ4UhuXq8Ch9sDr1d1La0pr6LA5VHgVf83gGakzCzh/GBmMZhZDGYOTlAF0cyZM/Hf//3fCOLgErUC35VU4srnNuG7kkqto6hy4pwT96w7hRPn5Lk2h4yZZZwfzCwGM4vBzMEJ6hyinTt3Ytu2bfj888/Ru3dvGI1Gv+c3bNjQLOGIiIiIRAiqIGrXrh3uueee5s5CREREpImgCqJVq1Y1dw4iIiIizQTdy+t2u7FlyxasWLECVVVVAIDi4mLY7fZmC0dEREQkQlBt9/n5+bjjjjtw6tQpOBwOHD16FFdccQUyMzNRV1eHt99+uyWyaopt9z9xur0oq3YgPsYMU0TzXR+npdvuXR4FtjoPLJHhMIZrc+0stW33ajProe2+peZHS2JmMZhZDGb216Jt9zNmzED//v1RXl6OqKgo3/J77rkH//73v4P5liQRU0QYOlmipPlFu8AYbkBCTIRmxVAwZMws4/xgZjGYWQxmDk5QP3nnzp147rnnYDKZ/JZ369YNRUVFzRKM9OtUWQ2mvb8fp8pqtI6iirXKhUVfnYG1yqV1lIDJmFnG+cHMYjCzGMwcnKAKIq/XC4/HU295YWEhYmNjmxyK9K2yzoXPDltRWSfPmzQA2J1e/OdUDexOb+ODdULGzDLOD2YWg5nFYObgBFUQDR8+HG+++abva4PBALvdjrlz54bU7TyIiIiodQiq7X7JkiUYMmQIrrnmGtTV1WHChAk4duwYEhISsG7duubOSERERNSigiqIkpOTcfDgQaxbtw7Z2dnwer2YPHkyHnjgAb+TrImIiIhkEFRBBABRUVF45JFH8MgjjzRnHpJAx7Zm/PH2K9GxrVnrKKrER0fgwWvbIT466GnfZGovLaCHzGrJOD+YWQxmFoOZgxPUdYjefffdyz7/4IMPBh1Ir3gdopbX0tchCgV6uA4REZGeBPr+HdSfnTNmzPD72uVyoaamBiaTCdHR0a2yIKKf2Gpd2Jt3DjekxMESZWz8BTphd3qRe7oOvRMj0cYkx/U5ZMws4/xgZjGYWQxmDk5Q/8KWl5f7Pex2O44cOYKbb76ZJ1WHgIJzNZjy7jcoOCfPNS6A89f0eWm7XNf0kTGzjPODmcVgZjGYOTjN9idnz5498fLLL9c7ekRERESkd816DD48PBzFxcXN+S1RVFSE3/72t4iPj0d0dDSuvfZa7N+/3/e8oiiYN28ekpOTERUVhcGDByM3N9fvezgcDkyfPh0JCQmIiYnBqFGjUFhY2Kw5iYiISF5BnUP0z3/+0+9rRVFQUlKCpUuX4qabbmqWYMD5j+ZuuukmDBkyBJ9//jk6duyIH3/8Ee3atfONefXVV7F48WKsXr0avXr1woIFCzB8+HAcOXLEd9XszMxM/Otf/8L69esRHx+PmTNnIiMjA/v370d4eHiz5SUiIiI5BVUQ3X333X5fGwwGdOjQAbfddhveeOON5sgFAHjllVfQpUsXrFq1yrese/fuvv9XFAVvvvkm5syZgzFjxgAA1qxZg8TERHzwwQeYOnUqbDYbVq5ciffeew/Dhg0DAKxduxZdunTBli1bcPvttzdb3lBhjghDz45tYJboxoEAYAo3oKvFCJNMN0qVMLOM84OZxWBmMZg5OEG13YtyzTXX4Pbbb0dhYSG2b9+OX/ziF5g2bRqmTJkCADhx4gR69OiB7Oxs9OvXz/e60aNHo127dlizZg22bt2KoUOH4ty5c2jfvr1vTN++fXH33Xdj/vz5Df5sh8MBh8Ph+7qyshJdunRh230LYtt907HtnojIX6Bt97ouH0+cOIHly5ejZ8+e+OKLL/DYY4/hySef9F0HyWq1AgASExP9XpeYmOh7zmq1wmQy+RVDF49pyKJFi2CxWHyPLl26NOeqERERkY4E9ZHZU089FfDYxYsXB/MjAABerxf9+/fHwoULAQD9+vVDbm4uli9f7netI4PB/+MERVHqLbtYY2Nmz57tt54XjhARkFtsw7gVe/Dh1BvRO9midZyAnTjnxDNZVrwyPAlXxJm0jhMQGTPLOD+YWQxmFoOZgxNUQXTgwAFkZ2fD7XbjyiuvBAAcPXoU4eHhuO6663zjGitKGtOpUydcc801fsuuvvpq/N///R8AICkpCcD5o0CdOnXyjSktLfUdNUpKSoLT6UR5ebnfUaLS0lIMGjTokj/bbDbDbJbnsuciKQpgd7ih3w9bG+ZVFNS6FHglCi5jZhnnBzOLwcxiMHNwgvrI7K677sKtt96KwsJCZGdnIzs7GwUFBRgyZAgyMjKwbds2bNu2DVu3bm1SuJtuuglHjhzxW3b06FF069YNAJCSkoKkpCRkZWX5nnc6ndi+fbuv2ElLS4PRaPQbU1JSgpycnMsWRERERBQ6gjpC9MYbb2Dz5s1+R1zat2+PBQsWID09HTNnzmyWcH/4wx8waNAgLFy4EGPHjsXevXvxl7/8BX/5y18AnD8ClZmZiYULF6Jnz57o2bMnFi5ciOjoaEyYMAEAYLFYMHnyZMycORPx8fGIi4vDrFmzkJqa6us6IyIiotAWVEFUWVmJ06dPo3fv3n7LS0tLUVVV1SzBAOD666/Hxo0bMXv2bLz44otISUnBm2++iQceeMA35umnn0ZtbS2mTZuG8vJyDBgwAJs3b/ZdgwgAlixZgoiICIwdOxa1tbUYOnQoVq9ezWsQEREREYAg2+4ffPBBbN++HW+88QZuvPFGAMCePXvwxz/+EbfccgvWrFnT7EG1xrvd/6TW6cGPZ+zo0aENokzNV1S2dNt9nduLQpsLnS1GREpyfQ61mfXQdt9S86MlMbMYzCwGM/sL9P07qIKopqYGs2bNwjvvvAOX6/xNJyMiIjB58mS89tpriImJCT65TrEganm8DlHT6aEgIiLSkxa9DlF0dDSWLVuGsrIyX8fZuXPnsGzZslZZDJG/oopaPP9RDooqarWOokpptRvL95ahtNqtdZSANXfmnJycRh9NJeP8YGYxmFkMZg5Okz43KCkpQUlJCXr16oWYmBjo+KLX1IzKq514b08+yqudWkdRpbLOg0+P2lFZ59E6SsBkzCzj/GBmMZhZDGYOTlAFUVlZGYYOHYpevXrhzjvvRElJCQDgd7/7XbN1mBERERGJElRB9Ic//AFGoxGnTp1CdHS0b/m4ceOwadOmZgtHREREJEJQbfebN2/GF198gc6dO/st79mzJ/Lz85slGBEREZEoQR0hqq6u9jsydMHZs2d5u4sQEN/GhMk3pyC+jRz31rqgXWQ4Rl8Vi3aRcrShAnJmlnF+MLMYzCwGMwcnqLb7kSNH4rrrrsNLL72E2NhYHDp0CN26dcP48ePh9Xrxj3/8oyWyaopt9y2PbfdN11jbfSDbmK37RNSatGjb/WuvvYYVK1ZgxIgRcDqdePrpp9GnTx989dVXeOWVV4IOTXKodrixP78c1Q552tcBoNblxfdnHKh1ebWOEjAZM8s4P5hZDGYWg5mDE1RBdM011+DQoUO44YYbMHz4cFRXV2PMmDE4cOAAevTo0dwZSWfyzlbj3uW7kHe2WusoqhRVuvDHL6woqnRpHSVgMmaWcX4wsxjMLAYzB0f1SdUulwvp6elYsWIF5s+f3xKZiIiIiIRSfYTIaDQiJycHBoOhJfIQERERCRfUR2YPPvggVq5c2dxZiIiIiDQR1HWInE4n/va3vyErKwv9+/evd/+yxYsXN0s40qfwMAPiYkwID5PrKGF4mAFtzWFS5ZY1s2zzg5nFYGYxmDk4qtruT5w4ge7du2Po0KGX/oYGA7Zu3dos4fSEbfctj233Tce2eyIif4G+f6s6QtSzZ0+UlJRg27ZtAM7fquPPf/4zEhMTm5aWiIiISEOqziG6+GDS559/jupqedr6qHkcPV2FW1/bhqOnq7SOokp+hRNTPipCfoU8d4CWMbOM84OZxWBmMZg5OEGdVH1BEBe5plbA6fYiv6wGTrc8FwsEAJdHQYndDZdHnnkrY2YZ5wczi8HMYjBzcFQVRAaDoV67PdvviYiISHaqziFSFAUPPfSQ7waudXV1eOyxx+p1mW3YsKH5EhIRERG1MFUF0aRJk/y+/u1vf9usYYiIiIi0ENTd7kMR2+5/UlXnQvapClzXtR1iI43N9n1buu2+xunF92cduDrBjGhTk06fE0ZtZj203bfU/GhJzCwGM4vBzP4Cff9mQRQgFkQtj9chajo9FERERHoS6Pu3HH8mk66UVtZhSdZRlFbWaR1FlXM1brz/bQXO1bi1jhIwGTPLOD+YWQxmFoOZg8OCiFQrrXLgv/99DKVVDq2jqHKu1oN1h204V+vROkrAZMws4/xgZjGYWQxmDg4LIiIiIgp5LIiIiIgo5LEgIiIiopDHgohUs0QZcfe1ybBEydHOeUEbcxgGd49BG7M8017GzDLOD2YWg5nFYObgsO0+QGy7b3lsu286tt0TEflj2z21mDqXByfPVqPOJU/nEwA4PQqKq1xwynSjVAkzyzg/mFkMZhaDmYPDgohUO15qx+DXv8TxUrvWUVQ5VeHEox8X41SFU+soAZMxs4zzg5nFYGYxmDk4LIiIiIgo5Km6uSsRtX6NnWfEc4yIqDXiESIiIiIKeSyIiIiIKOSx7T5AbLtveWy7lwM/MiMimbDtnoiIiChALIhItR/P2HHPsv/gxzPytHQCQKHNhZmbSlBoc2kdJWAyZpZxfjCzGMwsBjMHhwURqVbr9ODAqQrUOuW56BcA1Lm9OHLWiTq3V+soAZMxs4zzg5nFYGYxmDk4LIiIiIgo5LEgIiIiopDHgoiIiIhCHgsiUq1z+ygsGdcXndtHaR1FlcQ2EZg5KB6JbeS5QLuMmWWcH8wsBjOLwczB4XWIAsTrELU8XodIDrwOERHJhNchohZTZnfg3d0nUWZ3aB1FFVudB58cqYKtTp7OCxkzyzg/mFkMZhaDmYPDgohUK7HV4YWPc1Fiq9M6iipnqt14e985nKl2ax0lYDJmlnF+MLMYzCwGMwdHqoJo0aJFMBgMyMzM9C1TFAXz5s1DcnIyoqKiMHjwYOTm5vq9zuFwYPr06UhISEBMTAxGjRqFwsJCwemJiIhIr6QpiPbt24e//OUv+NWvfuW3/NVXX8XixYuxdOlS7Nu3D0lJSRg+fDiqqqp8YzIzM7Fx40asX78eO3fuhN1uR0ZGBjweeT6GICIiopYjRUFkt9vxwAMP4K9//Svat2/vW64oCt58803MmTMHY8aMQZ8+fbBmzRrU1NTggw8+AADYbDasXLkSb7zxBoYNG4Z+/fph7dq1OHz4MLZs2aLVKhEREZGOSFEQPf744xg5ciSGDRvmtzwvLw9WqxXp6em+ZWazGbfeeit27doFANi/fz9cLpffmOTkZPTp08c3piEOhwOVlZV+DzovxhyBX/dMQIxZnlZwAIgyhqFfp0hEGaWY9gDkzCzj/GBmMZhZDGYOju631vr165GdnY19+/bVe85qtQIAEhMT/ZYnJiYiPz/fN8ZkMvkdWbow5sLrG7Jo0SLMnz+/qfFbpZSEGLw3eYDWMVT7RVsjXhqa2PhAHZExs4zzg5nFYGYxmDk4uv6zs6CgADNmzMDatWsRGRl5yXEGg8Hva0VR6i27WGNjZs+eDZvN5nsUFBSoC9+KebwKqupc8HjluoSVx6ugxumVKresmWWbH8wsBjOLwczB0XVBtH//fpSWliItLQ0RERGIiIjA9u3b8ec//xkRERG+I0MXH+kpLS31PZeUlASn04ny8vJLjmmI2WxG27Zt/R503vcllUidtxnfl8j1MWJeuRNj/7cAeeVOraMETMbMMs4PZhaDmcVg5uDouiAaOnQoDh8+jIMHD/oe/fv3xwMPPICDBw/iiiuuQFJSErKysnyvcTqd2L59OwYNGgQASEtLg9Fo9BtTUlKCnJwc3xgiIiIKbbo+hyg2NrbebQJiYmIQHx/vW56ZmYmFCxeiZ8+e6NmzJxYuXIjo6GhMmDABAGCxWDB58mTMnDkT8fHxiIuLw6xZs5CamlrvJG0iIiIKTbouiALx9NNPo7a2FtOmTUN5eTkGDBiAzZs3IzY21jdmyZIliIiIwNixY1FbW4uhQ4di9erVCA8P1zA5ERER6YV0BdGXX37p97XBYMC8efMwb968S74mMjISb731Ft56662WDUdERERS4t3uA8S73f/E5fGistaFtlFGGMOb7zS0lr7bvduroNrpRYwpDBFhl+9C1As9Zm7sbvctNT9aEjOLwcxiMLO/QN+/pTtCRNozhochvo1Z6xiqRYQZYImU62NSGTPLOD+YWQxmFoOZgyNH6Ui6kl9Wjd+t2Yf8smqto6hSUuXCi9tKUVLl0jpKwGTMLOP8YGYxmFkMZg4OCyJSrarOjS3fl6Kqzq11FFWqnV7sLapFtdOrdZSAyZhZxvnBzGIwsxjMHBwWRERERBTyWBARERFRyGNBRERERCGPBRGpltg2Es+NvBqJbS99w109io+OwOTr2iM+Wp7mShkzyzg/mFkMZhaDmYPD6xAFiNchanktfR0iah6NXYeIiEhPeB0iajG2Ghd2Hj+Lm3+ZAEu0MaDX6KHYsTs8OGitw7VJkWhjluPaPjJmDmZ+aI2ZxWBmMZg5OPzIjFQrKK/B4x9ko6C8Rusoqljtbry84yysdnlaUWXMLOP8YGYxmFkMZg4OCyIiIiIKeSyIiIiIKOSxICIiIqKQx4KIVIs0hqF3cltEGuWaPuaIMPRob4I5Qp7cMmaWcX4wsxjMLAYzB4dt9wFi233T6KHLjJoH2+6JSCaBvn/LUz4SERERtRAWRKRaTpENveZ8jpwim9ZRVPnxnBN3f5CPH885tY4SMBkzyzg/mFkMZhaDmYPDCzNSUJwer9/XMnwkpigK3N7z/5WFjJmB+vNDBswsBjOLwczq8QgRERERhTwWRERERBTyWBARERFRyGPbfYDYdv+TOpcHp87VoGtcNCKN5284KsM5RA63F1a7G0ltIqS5ro8eMzfWdt/Q/NA7ZhaDmcVgZn+Bvn+zIAoQC6LLk6EgoubB6xARkUwCff9mlxmpVlheg7f+fRzTh/4SndtHax0nYKV2N9YftmF8qgUd28gx9fWYubHit7HMeiyoZJzTzCwGM4uhh8z6OAZPUqmoceHDbwpQUePSOooqlQ4PNv9oR6XDo3WUgDGzGDLOaWYWg5nF0ENmFkREREQU8lgQERERUchjQUREREQhjwURqZbQxozfD+6BhDZmraOo0i4qHPf1bot2UXK0oQLMLIqMc5qZxWBmMfSQmW33AWLb/eWx7Z4CpccuMyJqvQJ9/+YRIlLN7nBj949lsDvcWkdRpcblxSFrHWpc8tz0kJnFkHFOM7MYzCyGHjKzICLVTp6txv1/3YOTZ6u1jqJKcaUL/7XlNIor5WlFZWYxZJzTzCwGM4uhh8wsiIiIiCjksSAiIiKikMeCiIiIiEIeCyJSLSLcgKS2kYgIN2gdRZWIMAPio8MRESZPbmYWQ8Y5zcxiMLMYesjMtvsAse3+8th2T4Fi2z0RicS2eyIiIqIAsSAi1X6wVuLGhf/GD9ZKraOocrLciUkbCnGy3Kl1lIAxsxgyzmlmFoOZxdBDZhZEpJrbo8BaWQe3R65PW91eBWU1Hri98uRmZjFknNPMLAYzi6GHzCyIiIiIKOSxICIiIqKQx4KIiIiIQh7b7gPEtvuf2B1uHC60IbWzBW3MEQDkaLuvcXlxvMyJX8abEG2U42+B1phZj233Dc1pvWNmMZhZjJbMHOj7NwuiALEgujwZCiLSBz0WRETUevE6RNRirLY6vLLpB1htdVpHUeVsjRurD5TjbI1b6ygBY2YxZJzTzCwGM4uhh8wsiEi1s3YHln/5I87aHVpHUaWi1oN/5FaiotajdZSAMbMYMs5pZhaDmcXQQ2ZdF0SLFi3C9ddfj9jYWHTs2BF33303jhw54jdGURTMmzcPycnJiIqKwuDBg5Gbm+s3xuFwYPr06UhISEBMTAxGjRqFwsJCkatCREREOqbrgmj79u14/PHHsWfPHmRlZcHtdiM9PR3V1dW+Ma+++ioWL16MpUuXYt++fUhKSsLw4cNRVVXlG5OZmYmNGzdi/fr12LlzJ+x2OzIyMuDxyPMXLBEREbUcXZ9+vmnTJr+vV61ahY4dO2L//v245ZZboCgK3nzzTcyZMwdjxowBAKxZswaJiYn44IMPMHXqVNhsNqxcuRLvvfcehg0bBgBYu3YtunTpgi1btuD2228Xvl5EdGmNnaDPk7KJqCXo+gjRxWw2GwAgLi4OAJCXlwer1Yr09HTfGLPZjFtvvRW7du0CAOzfvx8ul8tvTHJyMvr06eMb0xCHw4HKykq/B53XLtqIcf27oF20UesoqrQ1hyO9Rxu0NYdrHSVgzCyGjHOamcVgZjH0kFmatntFUTB69GiUl5djx44dAIBdu3bhpptuQlFREZKTk31jH330UeTn5+OLL77ABx98gIcffhgOh/+JWunp6UhJScGKFSsa/Hnz5s3D/Pnz6y1n233D2HZPgWrsCA+PEBFRc2p1bfdPPPEEDh06hHXr1tV7zmAw+H2tKEq9ZRdrbMzs2bNhs9l8j4KCguCCt0J1Lg+Onq5CnUuuc7Acbi/yK5xwuL1aRwkYM4sh45xmZjGYWQw9ZJaiIJo+fTr++c9/Ytu2bejcubNveVJSEgDAarX6jS8tLUViYqJvjNPpRHl5+SXHNMRsNqNt27Z+DzrveKkd6Uu+wvFSu9ZRVCmwufD4JyUosLm0jhIwZhZDxjnNzGIwsxh6yKzrgkhRFDzxxBPYsGEDtm7dipSUFL/nU1JSkJSUhKysLN8yp9OJ7du3Y9CgQQCAtLQ0GI1GvzElJSXIycnxjSEiIqLQpusus8cffxwffPABPv74Y8TGxvqOBFksFkRFRcFgMCAzMxMLFy5Ez5490bNnTyxcuBDR0dGYMGGCb+zkyZMxc+ZMxMfHIy4uDrNmzUJqaqqv64yIxOH5ZkSkR7ouiJYvXw4AGDx4sN/yVatW4aGHHgIAPP3006itrcW0adNQXl6OAQMGYPPmzYiNjfWNX7JkCSIiIjB27FjU1tZi6NChWL16NcLD5emCISIiopaj64IokAY4g8GAefPmYd68eZccExkZibfeegtvvfVWM6YLbaZwXX/a2iCDwYCIsPon4esZM4sj45xmZjGYWQytM0vTdq813u3+8vgxCInCtnsiUqPVtd0TERERtRQWRKTa8dIqjPzzDhwvrWp8sI4U2FyY8alc7eDMLIaMc5qZxWBmMfSQmQURqVbn8iK3uBJ1LnkuvAecv2Dgj+VyXTCQmcWQcU4zsxjMLIYeMrMgIiIiopDHgoiIiIhCHgsiIiIiCnlsuw8Q2+5/YqtxYefxs7j5lwmwRBsByNF2b3d4cNBah2uTItHGLMdFOZk5OGpb8xua03rHzGIwsxgtmTnQ928WRAFiQXR5MhREFDp4rSIiuoDXIaIWc6bKgb/tOIEzVQ6to6hSXuvBxu8qUV7r0TpKwJhZDBnnNDOLwcxi6CEzCyJS7XRlHRZ8+j1OV9ZpHUWVsho3VmaXo6zGrXWUgDGzGDLOaWYWg5nF0ENmXd/LjPTj5x+JHS87X8EfP34cKDdrFYmIiKjZ8AgRERERhTwWRERERBTy+JEZAVDXJRZjCsMNv4hCjEmuelrG3MwsRmxkBIZd3RGxkfL8k8jMYjCzGHrIzLb7ALX2tnu2zVNrwrZ7Irog0PdvecpH0g23V0G104sYUxgiwgxaxwmYjLmZuWVc/AfAxZllKKhcHi8qa11oG2WEMVyOo3HMLAYzB0eOLUW6crLciQf+UYiT5U6to6giY25mFkPGzEesVUhbsAVHrFVaRwkYM4vBzMFhQUREREQhjwURERERhTwWRERERBTyWBARERFRyGPbfYDYdv8Tj1eBw63AHGFAuE67iBoiY25mDk5jXWIXz/eLM8vQZebxKqhxuhFtipBqbjBzy2Nmf2y7pxYTHmZAtEmOX7KfkzE3MwdH7XW19JBZrfAwA2IjjVrHUIWZxWDm4PAjM1KtqNKF5/99GkWVLq2jqCJjbmYWQ23mnJycyz5EyDtbjYkrv0be2WohP685MLMYzBwcFkSkWq3LiwMldah1ebWOooqMuZlZDBkzVzvc2HHsLKodbq2jBIyZxWDm4PAjMyKii/BWNkShhwVRiOA/8ERERJfGj8yIiIgo5LEgItU6xETgsevj0CFGrgOMMuZmZjFkzNzJEokXR/dGJ0uk1lECxsxiMHNweB2iAMl+HSJ+ZEYkjgzXMSIKFYG+f/MIEalW5fBg2wk7qhweraOoImNuZhZDxswVNU5sPFCIihqn1lECxsxiMHNwWBCRaqftbryxqwyn7fK0dAJy5mZmMWTMXFheiz98+C0Ky2u1jhIwZhaDmYPDgoiIiIhCHgsiIiIiCnksiIiIiCjkydNjSroRGRGGKxNMiIyQq56WMTczi9HcmRvr6myOLrQoUzj6dW2HKFN4k7+XKMwsBjMHh233AWLbPRE1F7blE4nDtnsiIiKiALEgItWOlzmQsTYfx8scWkdRRcbczCyGjJlzimzo/uynyCmyaR0lYMwsBjMHh+cQtRL8SIxIHiLOMSIidXiEiIiIiEIejxAREelMIEd8L3y8d/z4caDcXO95HmUiUodHiIiIiCjkse0+QHpvuxd5DpHTo+BsjRsJ0REwhRuE/dymkjE3M4sRipm1OIJU5/LAaqtDkiUSkUY5rpHDzGK0ZOZA379ZEAWIBRERtSb8SI1CBa9DRC3Ganfh9Z1nYbW7tI6iioy5mVmMUMyck5Nz2UdLKDhXg8z1B1BwrqZFvn9LYGYx9JA5pAqiZcuWISUlBZGRkUhLS8OOHTu0jiQlu8OLL09Ww+7wah1FFRlzM7MYzCyGrdaFjw4Ww1YrT+HJzGLoIXPIFEQffvghMjMzMWfOHBw4cAC//vWvMWLECJw6dUrraERERKSxkGm7X7x4MSZPnozf/e53AIA333wTX3zxBZYvX45FixZpnO7yeH4QEYnW1H93eI4SySYkCiKn04n9+/fj2Wef9Vuenp6OXbt2Nfgah8MBh+Ony/jbbOcvJ15ZWdlyQS/BbrcL/5mXU1PtgNdRg5pqO+xmeQ7JypibmcVg5ua3Z8+eestOnDuf+dC3B2EvMOOaa67RIJk69qpKeB01sFdVorJSfAfid999p/o1P9/OXWP7tUCq5teS2/nC+3ajPWRKCCgqKlIAKP/5z3/8lv/pT39SevXq1eBr5s6dqwDggw8++OCDDz5awaOgoOCytUJIHCG6wGDwrzoVRam37ILZs2fjqaee8n3t9Xpx7tw5xMfHX/I1waisrESXLl1QUFCgy3b+5tDa15HrJ7/Wvo6tff2A1r+OXL/gKYqCqqoqJCcnX3ZcSBRECQkJCA8Ph9Vq9VteWlqKxMTEBl9jNpthNvtfDr9du3YtFRFt27ZtlZP851r7OnL95Nfa17G1rx/Q+teR6xcci8XS6JiQ6DIzmUxIS0tDVlaW3/KsrCwMGjRIo1RERESkFyFxhAgAnnrqKUycOBH9+/fHwIED8Ze//AWnTp3CY489pnU0IiIi0ljIFETjxo1DWVkZXnzxRZSUlKBPnz747LPP0K1bN01zmc1mzJ07t97Hc61Ja19Hrp/8Wvs6tvb1A1r/OnL9Wh7vZUZEREQhLyTOISIiIiK6HBZEREREFPJYEBEREVHIY0FEREREIY8FEREREYU8FkQaW7ZsGVJSUhAZGYm0tDTs2LFD60hBWbRoEa6//nrExsaiY8eOuPvuu3HkyBG/MQ899BAMBoPf48Ybb9QosTrz5s2rlz0pKcn3vKIomDdvHpKTkxEVFYXBgwcjNzdXw8Tqde/evd46GgwGPP744wDk239fffUV7rrrLiQnJ8NgMOCjjz7yez6QfeZwODB9+nQkJCQgJiYGo0aNQmFhocC1uLTLrZ/L5cIzzzyD1NRUxMTEIDk5GQ8++CCKi4v9vsfgwYPr7dPx48cLXpNLa2wfBjInZd2HABr8fTQYDHjttdd8Y/S8DwN5X9DT7yELIg19+OGHyMzMxJw5c3DgwAH8+te/xogRI3Dq1Cmto6m2fft2PP7449izZw+ysrLgdruRnp6O6upqv3F33HEHSkpKfI/PPvtMo8Tq9e7d2y/74cOHfc+9+uqrWLx4MZYuXYp9+/YhKSkJw4cPR1VVlYaJ1dm3b5/f+l24svtvfvMb3xiZ9l91dTX69u2LpUuXNvh8IPssMzMTGzduxPr167Fz507Y7XZkZGTA4/GIWo1Lutz61dTUIDs7G88//zyys7OxYcMGHD16FKNGjao3dsqUKX77dMWKFSLiB6SxfQg0Pidl3YcA/NarpKQE77zzDgwGA+69916/cXrdh4G8L+jq97Dp95KnYN1www3KY4895rfsqquuUp599lmNEjWf0tJSBYCyfft237JJkyYpo0eP1i5UE8ydO1fp27dvg895vV4lKSlJefnll33L6urqFIvForz99tuCEja/GTNmKD169FC8Xq+iKHLvPwDKxo0bfV8Hss8qKioUo9GorF+/3jemqKhICQsLUzZt2iQseyAuXr+G7N27VwGg5Ofn+5bdeuutyowZM1o2XDNpaB0bm5OtbR+OHj1aue222/yWybQPL35f0NvvIY8QacTpdGL//v1IT0/3W56eno5du3ZplKr52Gw2AEBcXJzf8i+//BIdO3ZEr169MGXKFJSWlmoRLyjHjh1DcnIyUlJSMH78eJw4cQIAkJeXB6vV6rcvzWYzbr31Vmn3pdPpxNq1a/HII4/AYDD4lsu8/34ukH22f/9+uFwuvzHJycno06ePlPvVZrPBYDDUu0n1+++/j4SEBPTu3RuzZs2S6qgmcPk52Zr24enTp/Hpp59i8uTJ9Z6TZR9e/L6gt9/DkLl1h96cPXsWHo8HiYmJfssTExNhtVo1StU8FEXBU089hZtvvhl9+vTxLR8xYgR+85vfoFu3bsjLy8Pzzz+P2267Dfv379f95egHDBiAd999F7169cLp06exYMECDBo0CLm5ub791dC+zM/P1yJuk3300UeoqKjAQw895Fsm8/67WCD7zGq1wmQyoX379vXGyPY7WldXh2effRYTJkzwu5P4Aw88gJSUFCQlJSEnJwezZ8/Gt99+W+9G2HrV2JxsTftwzZo1iI2NxZgxY/yWy7IPG3pf0NvvIQsijf38r2/g/KS5eJlsnnjiCRw6dAg7d+70Wz5u3Djf//fp0wf9+/dHt27d8Omnn9b7JdebESNG+P4/NTUVAwcORI8ePbBmzRrfSZytaV+uXLkSI0aMQHJysm+ZzPvvUoLZZ7LtV5fLhfHjx8Pr9WLZsmV+z02ZMsX3/3369EHPnj3Rv39/ZGdn47rrrhMdVbVg56Rs+xAA3nnnHTzwwAOIjIz0Wy7LPrzU+wKgn99DfmSmkYSEBISHh9ercEtLS+tVyzKZPn06/vnPf2Lbtm3o3LnzZcd26tQJ3bp1w7FjxwSlaz4xMTFITU3FsWPHfN1mrWVf5ufnY8uWLfjd73532XEy779A9llSUhKcTifKy8svOUbvXC4Xxo4di7y8PGRlZfkdHWrIddddB6PRKOU+BerPydawDwFgx44dOHLkSKO/k4A+9+Gl3hf09nvIgkgjJpMJaWlp9Q5rZmVlYdCgQRqlCp6iKHjiiSewYcMGbN26FSkpKY2+pqysDAUFBejUqZOAhM3L4XDg+++/R6dOnXyHq3++L51OJ7Zv3y7lvly1ahU6duyIkSNHXnaczPsvkH2WlpYGo9HoN6akpAQ5OTlS7NcLxdCxY8ewZcsWxMfHN/qa3NxcuFwuKfcpUH9Oyr4PL1i5ciXS0tLQt2/fRsfqaR829r6gu9/DZj1Fm1RZv369YjQalZUrVyrfffedkpmZqcTExCgnT57UOppqv//97xWLxaJ8+eWXSklJie9RU1OjKIqiVFVVKTNnzlR27dql5OXlKdu2bVMGDhyo/OIXv1AqKys1Tt+4mTNnKl9++aVy4sQJZc+ePUpGRoYSGxvr21cvv/yyYrFYlA0bNiiHDx9W7r//fqVTp05SrNvPeTwepWvXrsozzzzjt1zG/VdVVaUcOHBAOXDggAJAWbx4sXLgwAFfl1Ug++yxxx5TOnfurGzZskXJzs5WbrvtNqVv376K2+3WarV8Lrd+LpdLGTVqlNK5c2fl4MGDfr+TDodDURRFOX78uDJ//nxl3759Sl5envLpp58qV111ldKvXz9drJ+iXH4dA52Tsu7DC2w2mxIdHa0sX7683uv1vg8be19QFH39HrIg0tj//M//KN26dVNMJpNy3XXX+bWpywRAg49Vq1YpiqIoNTU1Snp6utKhQwfFaDQqXbt2VSZNmqScOnVK2+ABGjdunNKpUyfFaDQqycnJypgxY5Tc3Fzf816vV5k7d66SlJSkmM1m5ZZbblEOHz6sYeLgfPHFFwoA5ciRI37LZdx/27Zta3BOTpo0SVGUwPZZbW2t8sQTTyhxcXFKVFSUkpGRoZt1vtz65eXlXfJ3ctu2bYqiKMqpU6eUW265RYmLi1NMJpPSo0cP5cknn1TKysq0XbGfudw6BjonZd2HF6xYsUKJiopSKioq6r1e7/uwsfcFRdHX76Hh/4cmIiIiClk8h4iIiIhCHgsiIiIiCnksiIiIiCjksSAiIiKikMeCiIiIiEIeCyIiIiIKeSyIiIiIKOSxICIiIqKQx4KIiIiIQh4LIiIiIgp5LIiIiIgo5P0/mN5y7xIbUDAAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# Define classifier\n", "classifier = mapclassify.PrettyBreaks(y=travel_times, k=10)\n", "\n", "# Plot histogram for public transport rush hour travel time\n", "grid[\"pt_r_t\"].plot.hist(bins=50, color=\"lightgray\", title=\"Pretty breaks\")\n", "\n", "# Add vertical lines for class breaks\n", "for break_point in classifier.bins:\n", " plt.axvline(break_point, linestyle=\"dashed\", linewidth=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_**Figure 6.66**. Histogram of the travel time values with 10 pretty breaks. Data source: Tenkanen & Toivonen 2020._" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the data using pretty breaks\n", "ax = grid.plot(\n", " figsize=(6, 4),\n", " column=\"pt_r_t\",\n", " linewidth=0,\n", " scheme=\"prettybreaks\",\n", " k=10,\n", " legend=True,\n", " legend_kwds={\"title\": \"Travel times (min)\", \"bbox_to_anchor\": (1.4, 1)},\n", ")\n", "\n", "# Set the x and y axis off and adjust padding around the subplot\n", "plt.axis(\"off\")\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_**Figure 6.67**. Static map of travel times visualized using the pretty breaks classification scheme. Data source: Tenkanen & Toivonen 2020._" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "Regardless of the number of classes, pretty breaks is not ideal for our data as it fails to capture the variation in the data. Compared to this map, the previous two versions using natural breaks and quantiles provide a more informative view of the travel times." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Custom map classification\n", "\n", "In case none of the existing classification schemes produce a desired output, we can also create a custom classification scheme using `mapclassify` and select which class interval values to use. Fixed intervals with gradually increasing travel times provide an intuitive way to display travel time data. While the pretty breaks classification scheme follows this approach, it didn’t work perfectly for our data. With our own classification scheme, we can show differences among the typical travel times, but avoid having classes distinguishing between long travel times. We'll create a custom classifier with fixed 10-minute intervals up to 90 minutes to achieve this. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "UserDefined\n", "\n", " Interval Count\n", "------------------------\n", "[ 0.00, 10.00] | 49\n", "( 10.00, 20.00] | 286\n", "( 20.00, 30.00] | 1071\n", "( 30.00, 40.00] | 2678\n", "( 40.00, 50.00] | 2697\n", "( 50.00, 60.00] | 2125\n", "( 60.00, 70.00] | 1631\n", "( 70.00, 80.00] | 1109\n", "( 80.00, 90.00] | 603\n", "( 90.00, 181.00] | 771" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "break_values = [10, 20, 30, 40, 50, 60, 70, 80, 90]\n", "classifier = mapclassify.UserDefined(y=travel_times, bins=break_values)\n", "classifier" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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UlBTYbDZHm3HjxuHIkSPYsmULtmzZgiNHjmDChAmSrx8RERH5CSETAMSmTZtcttm/f78AIHJycoQQQpSVlQm1Wi02btzoaJOfny+CgoLEli1bhBBC/PLLLwKA2Ldvn6PNTz/9JACIU6dOud0/g8EgAAiDwdCAtZKHarNVnC4sF9VmKzOYwQwfZBCRa1KOQ3ffv0N8WYw1lMFggEqlQrNmzQAAhw4dgsViQXJysqNNfHw8kpKSsHfvXgwdOhQ//fQTdDodevfu7Whzzz33QKfTYe/evejYsWOdWSaTCSaTyXG/vLxcmpXyglB1MDrERTLDzQzzlRycu+K6XVJS0k1lBMq2YgYReYIcxqHffKm6pqYGc+bMwbhx4xwXViosLIRGo0Hz5s2d2sbFxaGwsNDRJjY2ttbrxcbGOtrUZeHChY7vHOl0OrRu3dqDa+NdeaVVmP3vY8iT8FeEAynj7z8Vo8holTQjULYVM4jIE+QwDv2iILJYLBg7dizsdjs++OCDetsLIaBSqRz3f///N2pzvblz58JgMDhuubm5jeu8DJRVWfDpwVyUVVmY4UbGtl+NKDfZ6m98ExmBsq2YQUSeIIdxKPuCyGKxYPTo0cjOzkZGRobTZbf1ej3MZjNKS0udnlNUVIS4uDhHm8uXL9d63StXrjja1EWr1aJp06ZONyIiIgpMsi6IrhVDZ8+exfbt2xEdHe30eI8ePaBWq5GRkeFYVlBQgKysLPTt2xcA0KdPHxgMBuzfv9/R5ueff4bBYHC0ISIiImXz6ZeqjUYjzp0757ifnZ2NI0eOICoqCvHx8fjDH/6AzMxM/Pe//4XNZnN85ycqKgoajQY6nQ6TJ09GWloaoqOjERUVhVmzZqFLly4YPHgwAOCOO+7Agw8+iClTpmDlypUAgD/96U9ISUm54ReqiYiISFl8WhAdPHgQAwYMcNyfOXMmAGDSpElIT0/Hl19+CQC46667nJ63c+dO9O/fHwCwZMkShISEYPTo0aiursagQYOwZs0aBAcHO9p//PHHeP755x2z0YYPH17ntY8CVUwTLf7cvz1immiZ4UbGHzo3RbOw4Pob30RGoGwrZhCRJ8hhHKqEEMJn6X6kvLwcOp0OBoOB3ycKcFlZWfW2uZlp90RE5D3uvn/L+jtE5BlGkxU//VoMo0m6qeSBlHGssAZVFul+ZDCQthUziMgT5DAOWRApwIWrlXj8f/fhwtVKZriR8dftl3GpXLqpn4G0rZhBRJ4gh3HIgoiIiIgUjwURERERKR4LIiIiIlI8FkQKEBKsgr5pKEKCb/xTJcz4/xnR4cEICfL/9WCGfDKIyDU5jENOu3cTp90rB6fdExEFDk67JyIiInITCyIFOFVYjnsWfIdTheXMcCNj0ud5uFBqljQjULYVM4jIE+QwDlkQKYDVJlBYXgOrTbpPRwMpo7jKBqvd/9eDGfLJICLX5DAOWRARERGR4rEgIiIiIsVjQURERESKx2n3bvLnafdGkxXH8wzo0kqHJtoQZtST8eUPR3BbtAbh6hv/vXAz0+4DaVsxg4g8Qcpx6O77NwsiN/lzQUQNw+sQEREFDl6HiBwKDTV4e8spFBpqmOFGxprDpbhaZZU0I1C2FTOIyBPkMA5ZECnAVaMJy3f9iqtGEzPcyPj3iXKUVdskzQiUbcUMIvIEOYxDFkRERESkeCyIiIiISPFYEBEREZHisSBSgGbhaozp2RrNwtXMcCMjuX0TNNUGS5oRKNuKGUTkCXIYh5x27yZOu1cOTrsnIgocnHZPDjUWG85crkCNRbqZU4GUkVNmhslqlzQjULYVM4jIE+QwDlkQKcC5IiOSl3yPc0VGZriRMe2/Bcg1WCTNCJRtxQwi8gQ5jENep56oEfixGhFRYOEZIiIiIlI8FkRERESkeCyIFEITLP2uDpSMkCBApVJJmhEo24oZROQpvh6HnHbvJk67Vw53vh/kDn6HiIjI9zjtnoiIiMhNLIgU4FxRBR7++w84V1TBDDcyZnwt9bT7wNlWzCAiT5DDOGRBpAA1FjtOXCpHjUXKiw0GTsavpVJfmDFwthUziMgT5DAOWRARERGR4rEgIiIiIsVjQURERESKx2n3bvLnafeGKgv2nLuK+26LgS5czYx6Mj7ZmYm79KFoog2+qde60bT7QNpWzCAiT5ByHLr7/s2CyE3+XBBRw/A6REREgYPXISKHKxUm/N8P53GlwsQMNzI2/VKO0mqbpBmBsq2YQUSeIIdxyIJIAS6X12D+1ydxubyGGW5krMosRXGVVdKMQNlWzCAiT5DDOGRBRERERIrHgoiIiIgUjwURERERKR4LIgWIDA3B4DtiERkawgw3MnrdEoYIjXRDI5C2FTOIyBPkMA457d5NnHavHJx2T0QUODjtnhwsNjuKjSZYbNL9aF4gZRhqbLDapfs7IZC2FTOIyBPkMA5ZECnA6cIK9Ji/HacLK5jhRsb4f+fhQqlZ0oxA2VbMICJPkMM4ZEFEREREiseCiIiIiBTPpwXR999/j0ceeQTx8fFQqVTYvHmz0+NCCKSnpyM+Ph5hYWHo378/Tpw44dTGZDJh+vTpiImJQUREBIYPH468vDynNqWlpZgwYQJ0Oh10Oh0mTJiAsrIyideOiIiI/IVP55lWVlaia9euePLJJ/HYY4/VenzRokVYvHgx1qxZgw4dOmD+/PkYMmQITp8+jcjISABAamoqvvrqK2zcuBHR0dFIS0tDSkoKDh06hODg336tfNy4ccjLy8OWLVsAAH/6058wYcIEfPXVV95bWZKUuzPDOPOLiIjqIptp9yqVCps2bcLIkSMB/HZ2KD4+HqmpqZg9ezaA384GxcXF4e2338YzzzwDg8GAFi1aYN26dRgzZgwA4NKlS2jdujW++eYbDB06FCdPnkSnTp2wb98+9O7dGwCwb98+9OnTB6dOnULHjh3d6p8/T7u32QWqzFaEa0IQHKQKyAxPFUQ2u8ChI8ehDVHd9HrcKMvX24oZ3s8gItekHId+P+0+OzsbhYWFSE5OdizTarXo168f9u7dCwA4dOgQLBaLU5v4+HgkJSU52vz000/Q6XSOYggA7rnnHuh0OkebuphMJpSXlzvd/FVwkAqRoWpJ/7EPpIxwTVBArAcz5JNBRK7JYRzKtiAqLCwEAMTFxTktj4uLczxWWFgIjUaD5s2bu2wTGxtb6/VjY2MdbeqycOFCx3eOdDodWrdufVPr40vZVysxYdXPyL5ayQw3Ml757jLyyy2SZgTKtmIGEXmCHMahbAuia1Qq52pRCFFr2fWub1NX+/peZ+7cuTAYDI5bbm5uA3suH5UmK344exWVJisz3Mg4XFCDaot0FwcLpG3FDCLyBDmMQ9kWRHq9HgBqncUpKipynDXS6/Uwm80oLS112eby5cu1Xv/KlSu1zj79nlarRdOmTZ1uREREFJhkWxAlJCRAr9cjIyPDscxsNmP37t3o27cvAKBHjx5Qq9VObQoKCpCVleVo06dPHxgMBuzfv9/R5ueff4bBYHC0ISIiImXz6bR7o9GIc+fOOe5nZ2fjyJEjiIqKQps2bZCamooFCxYgMTERiYmJWLBgAcLDwzFu3DgAgE6nw+TJk5GWlobo6GhERUVh1qxZ6NKlCwYPHgwAuOOOO/Dggw9iypQpWLlyJYDfpt2npKS4PcOMiIiIAptPC6KDBw9iwIABjvszZ84EAEyaNAlr1qzBiy++iOrqakydOhWlpaXo3bs3tm3b5rgGEQAsWbIEISEhGD16NKqrqzFo0CCsWbPGcQ0iAPj444/x/PPPO2ajDR8+HMuWLfPSWvpeS10oXh/RGS11ocxwI+PZu6PQIkK6oRFI24oZROQJchiHsrkOkdz583WIlMCTF2Z097U8kUVERNLy++sQkeeUVZmx6XAeyqqk+wX3QMrYed6ICpNN0oxA2VbMICJPkMM4ZEGkAHml1Xjh06PIK61mhhsZ7+4txmWjdFM/A2lbMYOIPEEO45AFERERESkeCyIiIiJSPBZEREREpHgsiBQgTBOMbm2aIUwTXH9jZqBjjAahIdINjUDaVswgIk+QwzjktHs3cdq9vHHaPRER1YXT7omIiIjcxIJIAbLyDWg352tk5RuY4UZGyvocnCs2SZoRKNuKGUTkCXIYhyyIiIiISPFYEBEREZHisSAiIiIixWNBRERERIrHafdu8udp9zUWGwoNNdDrQhGqluYaD77O8NS0+xqLDd8fPIaY8BBoglWN7qurLF9vK2Z4P4OIXJNyHLr7/h3i0VSSpVB1MNrFRDDDzYz4SLXkGYGyrZhBRJ4gh3HIj8wUILekCqkbDyO3pIoZbmT8bc9VFBotkmYEyrZiBhF5ghzGIQsiBTBUW7D5yCUYqqV7kw+kjF0XKmE02SXNCJRtxQwi8gQ5jEMWRERERKR4/A4RkQ+582Vw/iYaEZH0eIaIiIiIFI8FkQLERmoxY1AiYiO1zHAj4/EuOkSFSTf9OpC2FTOIyBPkMA55HSI3+fN1iJTAU9chashreSuLH5kRETWeu+/fPEOkABU1Fuw+cwUVNdJ9ez+QMg5dqkaVWbpZZoG0rZhBRJ4gh3HIgkgBcoqrMOnD/cgplu76DoGUMW9HES5VSDcoA2lbMYOIPEEO45CzzEhR6vuI6lyxyUs9ISIiOeEZIiIiIlI8FkRERESkeCyIFEATEoS20eHQhEi3uwMlQx2sQssmIVDf5C/duxIo24oZROQpchiHnHbvJk67lzdPTZX3JE67JyLyPU67JyIiInITCyIFOFlQju5vZOBkQTkz6pFdasa4z3KRXWqWLCNQthUziMhT5DAOWRApgM0uUFJphs0u3aejgZRRbrIHxHowQz4ZROSaHMYhCyIiIiJSPBZEREREpHgsiIiIiEjxOO3eTf487b7SZMWpwgrcro9EhFaaX2vxdYanpt1XW+y4UGZBu2ZqhKlv7u+FG02X//16ZJ891ejXccXX+4MZRNQQUo5Dd9+/WRC5yZ8LIiXgdYiIiKguvA4RORQYqvHGf39BgaGaGfW4WmnF/x4swdVKq2QZgbKtmEFEniKHcciCSAGKjWas2pONYqN019YJlIyyGhu+OFWBshqbZBmBsq2YQUSeIodxyIKIiIiIFI8FERERESkeCyIiIiJSPBZECtA8QoMJ97RF8wgNM+rRNDQYD3dogqahwZJlBMq2YgYReYocxiGn3buJ0+7ljdPuiYioLpx2Tw7VZhuy8g2oNks3cypQMmqsdpwrNqHGapcsI1C2FTOIyFPkMA5ZECnAr1eMSHl/D369YmRGPfIMFqR+W4g8g0WyjEDZVswgIk+RwzhkQURERESKJ+uCyGq14uWXX0ZCQgLCwsJw66234vXXX4fd/v8/zhBCID09HfHx8QgLC0P//v1x4sQJp9cxmUyYPn06YmJiEBERgeHDhyMvL8/bq0NEREQyJeuC6O2338aKFSuwbNkynDx5EosWLcI777yD999/39Fm0aJFWLx4MZYtW4YDBw5Ar9djyJAhqKiocLRJTU3Fpk2bsHHjRuzZswdGoxEpKSmw2fidASIiIgJk/dPOP/30E0aMGIGHH34YANCuXTt88sknOHjwIIDfzg4tXboUL730EkaNGgUAWLt2LeLi4rBhwwY888wzMBgMWLVqFdatW4fBgwcDANavX4/WrVtj+/btGDp0qG9WzotUKqCJNgQqFTPqE6RSIUytQpCEIb9fD6mmeAbK/giUDCJyTQ7jUNbT7t966y2sWLEC27ZtQ4cOHXD06FEkJydj6dKlePzxx3H+/Hm0b98emZmZ6Natm+N5I0aMQLNmzbB27Vrs2LEDgwYNQklJCZo3b+5o07VrV4wcORKvvfZandkmkwkmk8lxv7y8HK1bt+a0e5nitHsiIqqLu9PuZX2GaPbs2TAYDLj99tsRHBwMm82GN998E48//jgAoLCwEAAQFxfn9Ly4uDjk5OQ42mg0Gqdi6Fqba8+vy8KFC29YLBEREVFgkfV3iD799FOsX78eGzZsQGZmJtauXYu//e1vWLt2rVM71XXn2IQQtZZdr742c+fOhcFgcNxyc3MbvyI+dvZyBYYs3o2zlyvqb6zwjItlZkz96hIulkn3i8uBsq2YQUSeIodxKOszRH/5y18wZ84cjB07FgDQpUsX5OTkYOHChZg0aRL0ej2A384CtWzZ0vG8oqIix1kjvV4Ps9mM0tJSp7NERUVF6Nu37w2ztVottFqtFKvldSarHWeLjDBJeLHBQMkw2wQuGiww26T7JDlQthUziMhT5DAOZX2GqKqqCkFBzl0MDg52TLtPSEiAXq9HRkaG43Gz2Yzdu3c7ip0ePXpArVY7tSkoKEBWVpbLgoiIiIiUQ9ZniB555BG8+eabaNOmDTp37ozDhw9j8eLFeOqppwD89lFZamoqFixYgMTERCQmJmLBggUIDw/HuHHjAAA6nQ6TJ09GWloaoqOjERUVhVmzZqFLly6OWWdERESkbLIuiN5//3288sormDp1KoqKihAfH49nnnkGr776qqPNiy++iOrqakydOhWlpaXo3bs3tm3bhsjISEebJUuWICQkBKNHj0Z1dTUGDRqENWvWIDhYul80JyIiIv8h62n3cuLPv3ZvqLZgf3YJeiVEQRemDsgMT027N5rtOHG5Bp3jQtFEc3OfKN9ouvzv1yP319ONfh1XfL0/mEFEDSHlOHT3/ZsFkZv8uSBSAl6HiIiI6hIQ1yEizyiqqMFnB/Pwx56tEBsZ6ncZ14qG0mobMn41Ykj7JmgeJs3Hnd7I+P228pTrC6u61sPThZW/H1fezCAi1+QwDmU9y4w8o6jchHe2nkZRuan+xjLOKK6y4qMjZSiusvp1RqBsq0A5rryRQUSuyWEcsiAiIiIixWNBRERERIrHgoiIiIgUjwWRAjQNVeOhLno0DZVuSrE3MppognBvm/Cbng7v64xA2VaBclx5I4OIXJPDOOS0ezdx2r3vyHFKvTu8Oe2e0/eJiOrm7vs3zxApgNlqR4GhGmYpfxTVCxkWm8DVSissEv7wqjcyAmVbBcpx5Y0MInJNDuOQBZECnLlcgT4Ld+DM5Qq/zsgpM+OJTfnIKTP7RUZWVladt29+PIw+C3fgmx8Pe6DHdfPGtgqU48obGUTkmhzGYaMKouzsbE/3g4iIiMhnGlUQ3XbbbRgwYADWr1+PmpoaT/eJiIiIyKsaVRAdPXoU3bp1Q1paGvR6PZ555hns37/f030jIiIi8opGFURJSUlYvHgx8vPzsXr1ahQWFuK+++5D586dsXjxYly5csXT/SQiIiKSjEem3ZtMJnzwwQeYO3cuzGYz1Go1xowZg7fffhstW7b0RD99zp+n3dvtAha7HeqgIAQFqfwu49qUcrsQsNmB4CAgSCXResgwozHT7uvK8PS0e38/rryZQUSuSTkOvTLt/uDBg5g6dSpatmyJxYsXY9asWfj111+xY8cO5OfnY8SIETfz8uQhQUEqaEOCJf3H3isZKhXUwSrJChVmNDAjUI4rL2QQkWtyGIeNKogWL16MLl26oG/fvrh06RI++ugj5OTkYP78+UhISMC9996LlStXIjMz09P9pUY4f8WIMSt/wvkrRr/OyC+3YM62QuSXW5ghg4xAOa68kUFErslhHIY05knLly/HU089hSeffBJ6vb7ONm3atMGqVatuqnPkGVVmG37OLkGV2ebXGdUWO7KKTKi2SHfhLma4L1COK29kEJFrchiHjSqIzp49W28bjUaDSZMmNebliYiIiLyqUR+ZrV69Gp999lmt5Z999hnWrl17050iIiIi8qZGFURvvfUWYmJiai2PjY3FggULbrpTRERERN7UqIIoJycHCQkJtZa3bdsWFy9evOlOkWfFNwvDW6O6IL5ZmF9ntIgIwfTeUWgR0ahPepnhYYFyXHkjg4hck8M4bNR1iNq0aYNly5Zh+PDhTsu/+OILTJs2DXl5eR7roFz483WI/N3119hRmsZch6ixr0NEFGgkvQ7R2LFj8fzzz2Pnzp2w2Wyw2WzYsWMHZsyYgbFjxza60ySNkkozNu6/iJJK6X753BsZhhobtp6tgKFGulkIzHBfoBxX3sggItfkMA4bVRDNnz8fvXv3xqBBgxAWFoawsDAkJydj4MCB/A6RDF0qq8acz4/jUlm1X2dcqbTi/Z9LcKXSygwZZATKceWNDCJyTQ7jsFFfMNBoNPj000/xxhtv4OjRowgLC0OXLl3Qtm1bT/ePiIiISHI39Y3LDh06oEOHDp7qCxEREZFPNKogstlsWLNmDb777jsUFRXBbne+Gu6OHTs80jki8hx+8ZqI6MYaVRDNmDEDa9aswcMPP4ykpCSoJPwBSbp54Zpg9E6IQrgm2K8zwtRBSIrVIkx9U79JzAwPCZTjyhsZROSaHMZho6bdx8TE4KOPPsJDDz0kRZ9kidPufYfT7j0z7d5TWURE/kTSafcajQa33XZboztH3mW3C5isNtjtDa595ZUhBCw2AXvDa3hmSJERKMeVFzKIyDU5jMNGFURpaWl477330IiTS+QDvxSUo+PLW/BLQblfZ5wvMePRTy7ifIl016lghvsC5bjyRgYRuSaHcdio7xDt2bMHO3fuxLfffovOnTtDrVY7Pf755597pHNERERE3tCogqhZs2Z49NFHPd0XIiIiIp9oVEG0evVqT/eDiIiIyGcaPSfXarVi+/btWLlyJSoqKgAAly5dgtFo9FjniIiIiLyhUdPuc3Jy8OCDD+LixYswmUw4c+YMbr31VqSmpqKmpgYrVqyQoq8+5c/T7s1WO4orTYiO0EITIs11aaTMuDal3GITMNTYoAsNhjpYmmtfyTGjMdPuG7seDZl27+/HlTcziMg1KcehpNPuZ8yYgZ49e6K0tBRhYWGO5Y8++ii+++67xrwkSUgTEoSWujBJ/7H3RoY6WIWYiBDJChVmNEygHFfeyCAi1+QwDhuVvGfPHrz88svQaDROy9u2bYv8/HyPdIw852JxFaZ+fAgXi6v8OqOwwoKF319BYYWFGTLICJTjyhsZROSaHMZhowoiu90Om81Wa3leXh4iIyNvulPkWeU1FnxzvBDlNdK9OXojw2i248eLVTCa7fU3ZobkGYFyXHkjg4hck8M4bFRBNGTIECxdutRxX6VSwWg0Yt68eYr6OQ8iIiIKDI2adr9kyRIMGDAAnTp1Qk1NDcaNG4ezZ88iJiYGn3zyiaf7SERERCSpRhVE8fHxOHLkCD755BNkZmbCbrdj8uTJGD9+vNOXrImIiIj8QaMKIgAICwvDU089haeeesqT/SEJxDbV4i9DOyK2qdavM6LDQzDxrmaIDm/0YeuXGY35JXtvrEegHFfeyCAi1+QwDht1HaKPPvrI5eMTJ05sdIfkyp+vQ+TvGlMQUOM05DpERET+wN3370b9+Thjxgyn+xaLBVVVVdBoNAgPDw/IgsifGaot2J9dgl4JUdCFqet/gkwzjGY7TlyuQee4UDTRSHOtCma4L1COK29kEJFrchiHjfqXsrS01OlmNBpx+vRp3HffffxStQzlllRhykcHkVsi3fUdvJFRWGHBG7ulv34PM9wTKMeVNzKIyDU5jEOP/emYmJiIt956q9bZIyIiIiK58+i59ODgYFy6dMmTL4n8/Hz8z//8D6KjoxEeHo677roLhw4dcjwuhEB6ejri4+MRFhaG/v3748SJE06vYTKZMH36dMTExCAiIgLDhw9HXl6eR/tJRERE/qtR3yH68ssvne4LIVBQUIBly5bh3nvv9UjHgN8+mrv33nsxYMAAfPvtt4iNjcWvv/6KZs2aOdosWrQIixcvxpo1a9ChQwfMnz8fQ4YMwenTpx1XzU5NTcVXX32FjRs3Ijo6GmlpaUhJScGhQ4cQHBzssf4SERGRf2pUQTRy5Ein+yqVCi1atMDAgQPx7rvveqJfAIC3334brVu3xurVqx3L2rVr5/h/IQSWLl2Kl156CaNGjQIArF27FnFxcdiwYQOeeeYZGAwGrFq1CuvWrcPgwYMBAOvXr0fr1q2xfft2DB061GP9lSttSBASY5tAK+GP5nkjQxOsQhudGhopf7CUGW4LlOPKGxlE5JocxmGjpt17S6dOnTB06FDk5eVh9+7duOWWWzB16lRMmTIFAHD+/Hm0b98emZmZ6Natm+N5I0aMQLNmzbB27Vrs2LEDgwYNQklJCZo3b+5o07VrV4wcORKvvfZandkmkwkmk8lxv7y8HK1bt+a0ex/gtHvv4bR7Igo07k67l/WfROfPn8fy5cuRmJiIrVu34tlnn8Xzzz/vuA5SYWEhACAuLs7peXFxcY7HCgsLodFonIqh69vUZeHChdDpdI5b69atPblqREREJCON+shs5syZbrddvHhxYyIAAHa7HT179sSCBQsAAN26dcOJEyewfPlyp2sdqVTOHwsIIWotu159bebOneu0ntfOEPmjE5cMGLNyHz595h50jtf5bcb5EjNmZxTi7SF63BqlYYaPMwLluPJGBhG5Jodx2KiC6PDhw8jMzITVakXHjh0BAGfOnEFwcDC6d+/uaFdfUVKfli1bolOnTk7L7rjjDvznP/8BAOj1egC/nQVq2bKlo01RUZHjrJFer4fZbEZpaanTWaKioiL07dv3htlarRZabWBcyl8IwGiyQsoPR72RYRcC1RYBu4QhzHBfoBxX3sggItfkMA4b9ZHZI488gn79+iEvLw+ZmZnIzMxEbm4uBgwYgJSUFOzcuRM7d+7Ejh07bqpz9957L06fPu207MyZM2jbti0AICEhAXq9HhkZGY7HzWYzdu/e7Sh2evToAbVa7dSmoKAAWVlZLgsiIiIiUo5GnSF69913sW3bNqczLs2bN8f8+fORnJyMtLQ0j3TuhRdeQN++fbFgwQKMHj0a+/fvxz//+U/885//BPDbGajU1FQsWLAAiYmJSExMxIIFCxAeHo5x48YBAHQ6HSZPnoy0tDRER0cjKioKs2bNQpcuXRyzzoiIiEjZGlUQlZeX4/Lly+jcubPT8qKiIlRUVHikYwBw9913Y9OmTZg7dy5ef/11JCQkYOnSpRg/fryjzYsvvojq6mpMnToVpaWl6N27N7Zt2+a4BhEALFmyBCEhIRg9ejSqq6sxaNAgrFmzhtcgIiIiIgCNnHY/ceJE7N69G++++y7uueceAMC+ffvwl7/8BQ888ADWrl3r8Y76mj//2n212YZfrxjRvkUThGmkKQKlzLg27b7GakeewYJWOjVCJbpWhdIzGjLt3t+PK29mEJFrUo5Dd9+/G1UQVVVVYdasWfjwww9hsfz245EhISGYPHky3nnnHURERDS+5zLlzwWRv+N1iLyH1yEiokAj6XWIwsPD8cEHH6C4uNgx46ykpAQffPBBQBZD/i6/rBqvbM5Cflm1X2cUVVqxfH8xiiqtzPBhRlZWFrKysrDj5yOY9uFu7Pj5iGPZtZunBMqxS0SuyWEc3tT5+oKCAhQUFKBDhw6IiIiAjC96rWillWas25eD0kqzX2eU19jw9RkjymtszFBIRqAcu0TkmhzGYaMKouLiYgwaNAgdOnTAQw89hIKCAgDA008/7bEZZkRERETe0qiC6IUXXoBarcbFixcRHh7uWD5mzBhs2bLFY50jIiIi8oZGTbvftm0btm7dilatWjktT0xMRE5Ojkc6RkREROQtjTpDVFlZ6XRm6JqrV68GzM9dBJLoJhpMvi8B0U2k+U0rb2U0Cw3GiNsj0SxUuqnRzJBXRqAcu0TkmhzGYaOm3T/88MPo3r073njjDURGRuLYsWNo27Ytxo4dC7vdjn//+99S9NWnOO3edzjt3nvcmXbvzv7g9H0ikgt3378b9ZHZO++8g/79++PgwYMwm8148cUXceLECZSUlODHH39sdKdJGpUmK04VVuB2fSQitI3a5bLIqLbYcaHMgnbN1AhTS3NBQ2bIKyNQjl0ick0O47BR/4p16tQJx44dQ69evTBkyBBUVlZi1KhROHz4MNq3b+/pPtJNyr5aiceW70X21Uq/zsgvt+AvWwuRX25hhkIyAuXYJSLX5DAOG1yGWSwWJCcnY+XKlXjttdek6BMRERGRVzX4DJFarUZWVhZUKpUU/SEiIiLyukZ9ZDZx4kSsWrXK030hIiIi8olGfXPJbDbj//7v/5CRkYGePXvW+v2yxYsXe6Rz5BnBQSpERWgQHCTdWT1vZTTVBjFDYRmBcOwSkWtyGIcNmnZ//vx5tGvXDoMGDbrxC6pU2LFjh0c6Jyecdu87nHbvPZx2T0SBRpJp94mJiSgoKMDOnTsB/PZTHX//+98RFxd3c70lIiIi8qEGfYfo+pNJ3377LSorOVVV7s5crkC/d3bizOUKv87IKTNjyuZ85JRJ92vIzJBXRqAcu0TkmhzG4U1dTa0RF7kmHzBb7cgproLZavfrDItNoMBohcUm3XHHDHllBMqxS0SuyWEcNqggUqlUtabbc/o9ERER+bsGfYdICIEnnnjC8QOuNTU1ePbZZ2vNMvv8888910MiIiIiiTWoIJo0aZLT/f/5n//xaGeIiIiIfKFRv3avRP487b6ixoLMi2Xo3qYZIkPVfpdxbZp3ldmOk1dNuCNGi3CNND8mqvSMhky7d5XhqWn3/n7sEpF7pByH7r5/syBykz8XRP6O1yHyHl6HiIgCjbvv39L8eUqyUlRegyUZZ1BUXuPXGSVVVnx8tAwlVVZmKCQjUI5dInJNDuOQBZECFFWY8N53Z1FUYfLrjJJqGz45bkBJtY0ZCskIlGOXiFyTwzhkQURERESKx4KIiIiIFI8FERERESkeCyIF0IWpMfKueOjCpJtS7I2MJtog9G8XgSZa6Q5bZsgrI1COXSJyTQ7jkNPu3cRp977Daffew2n3RBRoOO2eHGosNly4Wokai3SzgbyRYbYJXKqwwCzlj4kyQ1YZgXLsEpFrchiHLIgU4FyREf3/tgvniox+nXGxzIw/fXEJF8vMzFBIRqAcu0TkmhzGIQsiIiIiUrwG/bgrEZE7+D0jIvI3PENEREREiseCiIiIiBSP0+7dxGn3vsNp94GJH5kRkTdw2j0RERGRm1gQKcCvV4x49IMf8esV6aYzeiMjz2BB2pYC5BkszFBIRqAcu0TkmhzGIQsiBag223D4YhmqzdJd8MobGTVWO05fNaPGameGQjIC5dglItfkMA5ZEBEREZHisSAiIiIixWNBRERERIrHgkgBWjUPw5IxXdGqeZhfZ8Q1CUFa32jENZHuAuvMkFdGoBy7ROSaHMYhr0PkJl6HyHd4HaLAxOsQEZE38DpE5FBsNOGjny6g2Gjy6wxDjQ3/PV0BQ410sxCYIa+MQDl2icg1OYxDFkQKUGCowatfnECBocavM65UWrHiQAmuVFqZoZCMQDl2icg1OYxDvyqIFi5cCJVKhdTUVMcyIQTS09MRHx+PsLAw9O/fHydOnHB6nslkwvTp0xETE4OIiAgMHz4ceXl5Xu49ERERyZXfFEQHDhzAP//5T9x5551OyxctWoTFixdj2bJlOHDgAPR6PYYMGYKKigpHm9TUVGzatAkbN27Enj17YDQakZKSApuNF2IjIiIiPymIjEYjxo8fj//93/9F8+bNHcuFEFi6dCleeukljBo1CklJSVi7di2qqqqwYcMGAIDBYMCqVavw7rvvYvDgwejWrRvWr1+P48ePY/v27b5aJSIiIpIRvyiIpk2bhocffhiDBw92Wp6dnY3CwkIkJyc7lmm1WvTr1w979+4FABw6dAgWi8WpTXx8PJKSkhxt6mIymVBeXu5081cR2hDcnxiDCK1006O9kRGmDkK3lqEIU0t32DJDXhmBcuwSkWtyGIey/xdg48aNyMzMxIEDB2o9VlhYCACIi4tzWh4XF4ecnBxHG41G43Rm6Vqba8+vy8KFC/Haa6/dbPdlISEmAusm9/b7jFuaqvHGoLj6GzIjYDIC5dglItfkMA5lfYYoNzcXM2bMwPr16xEaGnrDdiqVyum+EKLWsuvV12bu3LkwGAyOW25ubsM6LyM2u0BFjQU2u3SXnPJWRpXZzgyFZQTCsUtErslhHMq6IDp06BCKiorQo0cPhISEICQkBLt378bf//53hISEOM4MXX+mp6ioyPGYXq+H2WxGaWnpDdvURavVomnTpk43f3WyoBxd0rfhZIF0H/t5IyO71IzR/8pFdqmZGQrJCJRjl4hck8M4lHVBNGjQIBw/fhxHjhxx3Hr27Inx48fjyJEjuPXWW6HX65GRkeF4jtlsxu7du9G3b18AQI8ePaBWq53aFBQUICsry9GGiIiIlE3W3yGKjIysdXn/iIgIREdHO5anpqZiwYIFSExMRGJiIhYsWIDw8HCMGzcOAKDT6TB58mSkpaUhOjoaUVFRmDVrFrp06VLrS9pERESkTLIuiNzx4osvorq6GlOnTkVpaSl69+6Nbdu2ITIy0tFmyZIlCAkJwejRo1FdXY1BgwZhzZo1CA4O9mHPiYiISC78riDatWuX032VSoX09HSkp6ff8DmhoaF4//338f7770vbOSIiIvJL/LV7N/nzr91bbHaUV1vQNEwNdbA0XxuTMuPar91b7QKVZjsiNEEICXI9i7CxmOG9DHd+7d7fj10ico+U49Dd92+/O0NEDacODkJ0E63fZ4QEqaALlfZjTmbIKyNQjl0ick0O45B/DilATnElnl57ADnFlX6dUVBhwes7i1BQYWGGQjIC5dglItfkMA5ZEClARY0V208WoaLG6tcZlWY79udXo9JsZ4ZCMgLl2CUi1+QwDlkQERERkeKxICIiIiLFY0FEREREiseCSAHimobi5YfvQFzTG/9Arj9kRIeHYHL35ogOl25yJDPklREoxy4RuSaHccjrELnJn69D5O+uXYeIAos71yEiIrpZvA4RORiqLNhz7iruuy0GunC1rDIaUuwYTTYcKazBXfpQNNFKc/0bZsgrQ87HLhF5jhzGIT8yU4Dc0ipM25CJ3NIqv84oNFrx1g9XUWiUblomM+SVESjHLhG5JodxyIKIiIiIFI8FERERESkeCyIiIiJSPBZEChCqDkLn+KYIVUu3u72RoQ0JQvvmGmhDmKGUjEA5donINTmMQ067dxOn3UuDU+qVi9Puicgb3H3/5p9EREREpHgsiBQgK9+ADi99i6x8g19n/FpixsgNOfi1xMwMhWQEyrFLRK7JYRzywowKYbbZvZ7h6Y/DhBCw2n/7r1SYIa8MwDfHLhF5n6/HIc8QERERkeKxICIiIiLFY0FEREREisdp927y52n3NRYbLpZUoU1UOELV0vwIZ10Znv4OkclqR6HRCn2TEMmufcMM72W4M+3eV8cuEXmXlOPQ3fdvFkRu8ueCyFd4jSFyhdchIiJvcPf9m7PMFCCvtArvf3cO0wfdhlbNw/02o8hoxcbjBoztokNsE2kOXWZ4L8OdgtmdjJstrLxx7BKRa3IYh/wOkQKUVVnw6cFclFVZ/Dqj3GTDtl+NKDfZmMEMj/HGsUtErslhHLIgIiIiIsVjQURERESKx4KIiIiIFI8FkQLENNHiz/3bI6aJ1q8zmoUF4w+dm6JZmHRTo5mhvAxvHLtE5JocxiGn3buJ0+4bjtPuyRs4fZ+IXHH3/ZtniBTAaLLip1+LYTRZ/TqjymLHscIaVFmk+wFAZigvwxvHLhG5JodxyIJIAS5crcTj/7sPF65W+nXGpXIL/rr9Mi6VSzctkxnKy/DGsUtErslhHLIgIiIiIsVjQURERESKx4KIiIiIFI8FkQKEBKugbxqKkGCVf2cEqRAdHoyQIGYww4MZXjh2icg1OYxDTrt3E6fdNxyn3ZM3cNo9EbnCafdEREREbmJBpACnCstxz4LvcKqw3K8zLpSaMenzPFwoNTODGR7jjWOXiFyTwzhkQaQAVptAYXkNrDbpPh31SoZdoLjKBqudGczwYIYXjl0ick0O45AFERERESkeCyIiIiJSPBZEREREpHicdu8mf552bzRZcTzPgC6tdGiiDfFahqen3VdZ7DhXbMZt0RqEq6Wp5Znhfxk3O+3eG+ODiFyTchy6+/7NgshN/lwQ+QqvQ0TewOsQEZErvA4RORQaavD2llMoNNT4dcbVKivWHC7F1SorM5jhMd44donINTmMQxZECnDVaMLyXb/iqtHk1xll1Tb8+0Q5yqptzGCGx3jj2CUi1+QwDmVdEC1cuBB33303IiMjERsbi5EjR+L06dNObYQQSE9PR3x8PMLCwtC/f3+cOHHCqY3JZML06dMRExODiIgIDB8+HHl5ed5cFSIiIpIxWRdEu3fvxrRp07Bv3z5kZGTAarUiOTkZlZWVjjaLFi3C4sWLsWzZMhw4cAB6vR5DhgxBRUWFo01qaio2bdqEjRs3Ys+ePTAajUhJSYHNJt1fnUREROQ/ZD2lYsuWLU73V69ejdjYWBw6dAgPPPAAhBBYunQpXnrpJYwaNQoAsHbtWsTFxWHDhg145plnYDAYsGrVKqxbtw6DBw8GAKxfvx6tW7fG9u3bMXToUK+vFxF5l6sv+J8r5kdlRCTzM0TXMxgMAICoqCgAQHZ2NgoLC5GcnOxoo9Vq0a9fP+zduxcAcOjQIVgsFqc28fHxSEpKcrSpi8lkQnl5udPNXzULV2NMz9ZoFq7264ym2mAkt2+CptpgZjDD4xlSHrtE5Jo33kPq4zfT7oUQGDFiBEpLS/HDDz8AAPbu3Yt7770X+fn5iI+Pd7T905/+hJycHGzduhUbNmzAk08+CZPJ+a/A5ORkJCQkYOXKlXXmpaen47XXXqu1nNPu3cdp9+QN7ky7d+dY5PR9osAUcNPun3vuORw7dgyffPJJrcdUKpXTfSFErWXXq6/N3LlzYTAYHLfc3NzGdVwGaiw2nLlcgRqLdN+Z8kaGyWpHTpkZJqudGczweIaUxy4RueaN95D6+EVBNH36dHz55ZfYuXMnWrVq5Viu1+sBAIWFhU7ti4qKEBcX52hjNptRWlp6wzZ10Wq1aNq0qdPNX50rMiJ5yfc4V2T064xcgwXT/luAXIOFGczweIaUxy4RueaN95D6yLogEkLgueeew+eff44dO3YgISHB6fGEhATo9XpkZGQ4lpnNZuzevRt9+/YFAPTo0QNqtdqpTUFBAbKyshxtiIiISNlkPcts2rRp2LBhA7744gtERkY6zgTpdDqEhYVBpVIhNTUVCxYsQGJiIhITE7FgwQKEh4dj3LhxjraTJ09GWloaoqOjERUVhVmzZqFLly6OWWdE5L/4XTUi8gRZF0TLly8HAPTv399p+erVq/HEE08AAF588UVUV1dj6tSpKC0tRe/evbFt2zZERkY62i9ZsgQhISEYPXo0qqurMWjQIKxZswbBwdLNXCEiIiL/IeuCyJ0JcCqVCunp6UhPT79hm9DQULz//vt4//33Pdg7/6IJlv7TUakzVCoVQoJqf4meGczwRAYR+ZY33qdc8Ztp977GX7tvOH6UQf6E0+6JAlPATbsnIiIikgoLIgU4V1SBh//+A84VVdTfWMYZuQYLZnwt/RRsZigzQ8pjl4hc88Z7SH1YEClAjcWOE5fKUWOR7uJ23sgwWe34tVT6i/QxQ5kZUh67ROSaN95D6sOCiIiIiBSPBREREREpHgsiIiIiUjxOu3eTP0+7N1RZsOfcVdx3Wwx04WqvZXh62r3RZMORwhrcpQ9FE600F9VkBjNc4dR8ImlI+T7l7vs3CyI3+XNB5Cu8DhEFGhZERP6H1yEihysVJvzfD+dxpcLk1xml1TZs+qUcpdU2ZjDDrzKIyDVvvIfUhwWRAlwur8H8r0/icnmNX2cUV1mxKrMUxVVWZjDDrzKIyDVvvIfUR9a/ZUbydf3HYeeKf6vqz507B5RqfdElIiKiRuMZIiIiIlI8FkRERESkePzITAEiQ0Mw+I5YRIa6t7sbMzssQhOEXreEIUIjXY3NDGb4awYRudbQ9ykpcNq9m5Q07Z7T5Ynqxmn3RP7H3fdvniFSAIvNjvJqC5qGqaEOluavYKtdoNJsR4QmCCFBKmYwQ7EZ7vxBwcKKyJk33qfqw3PECnC6sAI95m/H6cIKyTIulJox/t95uFBqZgYzmEFEDeKN96n6sCAiIiIixWNBRERERIrHgoiIiIgUjwURERERKR6n3bvJn6fd2+wCVWYrwjUhCHZjFk1jpt3b7AImq4A2ROVWRmMwgxm+znBndhhnmRE1XEPfpxqC0+7JIThIhchQteQZ4Rpp3rCYwQy5ZPAaXUTS8Mb7VH34kZkCZF+txIRVPyP7aqVkGfnlFrzy3WXkl1uYwQxmeEBWVla9N6JA4Y33qfqwIFKASpMVP5y9ikqTVbKMaosdhwtqUG2xM4MZzCCiBvHG+1R9+JEZEZGX8ewOkfywIFIY/kNMRERUGz8yIyIiIsVjQaQALXWheH1EZ7TUhUqW0SIiBM/eHYUWEdKddGQGM/w1g4hc88b7VH14HSI3+fN1iH6PH5kRBQ5ez4iofu6+f/MMkQKUVZmx6XAeyqqk+zXvCpMNO88bUWGyMYMZzCCiBvHG+1R9WBApQF5pNV749CjySqsly7hstOLdvcW4bJRuyiQzmOGvGUTkmjfep+rDgoiIiIgUjwURERERKR4LIiIiIlI8zjNVgDBNMLq1aYYwTTCk+nQ2NCQIHWM0CA2RrsZmBjP8NUMq7swa5Uw08ge/f5/yFU67dxOn3RORP2JBRErHafdEREREbmJBpABZ+Qa0m/M1svINkmWcKzYhZX0OzhWbmMEMZhBRg3jjfao+/A5RALnRx2HX/qE/d+4cbovWerNLRORj/J4RkXt4hoiIiIgUj2eIiIgUzlOTLXimifwZzxARERGR4nHavZv8Ydr9jf7KM9sErlZZERMeAk2wSpJsZjCDGeQOnkWiutRYbCg01ECvC0Wo2rPXInL3/ZsfmSmAJliF+Eg1M5jBDB9lEJFroepgtIuJ8Gkf+JGZAhQaLfjbnqsoNFqYwQxm+CCD/r+srKx6b6Q8uSVVSN14GLklVT7rg6IKog8++AAJCQkIDQ1Fjx498MMPP/i6S15hNNmx60IljCY7M5jBDB9kEJFrhmoLNh+5BEO17/4wUUxB9OmnnyI1NRUvvfQSDh8+jPvvvx/Dhg3DxYsXfd01IiIi8jHFfIdo8eLFmDx5Mp5++mkAwNKlS7F161YsX74cCxcu9HHv6sfTyESkFLwMAPmCIgois9mMQ4cOYc6cOU7Lk5OTsXfv3jqfYzKZYDL9/0v5Gwy/XU68vLxcuo66YDQaG/3cqkoT7KYqVFUaYdRKczqSGcxgBsnNvn376m3TqVMnL/QksP3yyy83/RrnS34bh8aKcpSXe3a257X37Xon1QsFyM/PFwDEjz/+6LT8zTffFB06dKjzOfPmzRMAeOONN9544423ALjl5ua6rBUUcYboGpXKueoUQtRads3cuXMxc+ZMx3273Y6SkhJER0ff8DmNUV5ejtatWyM3N1e21zeSilLXnevN9VYKpa4711te6y2EQEVFBeLj4122U0RBFBMTg+DgYBQWFjotLyoqQlxcXJ3P0Wq10Gqdfwi1WbNmUnURTZs2ldUB5E1KXXeut7Iodb0B5a4711s+dDpdvW0UMctMo9GgR48eyMjIcFqekZGBvn37+qhXREREJBeKOEMEADNnzsSECRPQs2dP9OnTB//85z9x8eJFPPvss77uGhEREfmYYgqiMWPGoLi4GK+//joKCgqQlJSEb775Bm3btvVpv7RaLebNm1fr4zklUOq6c7253kqh1HXnevvnevPHXYmIiEjxFPEdIiIiIiJXWBARERGR4rEgIiIiIsVjQURERESKx4KIiIiIFI8FkY998MEHSEhIQGhoKHr06IEffvjB113yqIULF+Luu+9GZGQkYmNjMXLkSJw+fdqpzRNPPAGVSuV0u+eee3zUY89IT0+vtU56vd7xuBAC6enpiI+PR1hYGPr3748TJ074sMee0a5du1rrrVKpMG3aNACBta+///57PPLII4iPj4dKpcLmzZudHndnH5tMJkyfPh0xMTGIiIjA8OHDkZeX58W1aDhX622xWDB79mx06dIFERERiI+Px8SJE3Hp0iWn1+jfv3+t42Ds2LFeXpOGqW9/u3Ns++P+Bupf97rGvEqlwjvvvONo4w/7nAWRD3366adITU3FSy+9hMOHD+P+++/HsGHDcPHiRV93zWN2796NadOmYd++fcjIyIDVakVycjIqKyud2j344IMoKChw3L755hsf9dhzOnfu7LROx48fdzy2aNEiLF68GMuWLcOBAweg1+sxZMgQVFRU+LDHN+/AgQNO63zt6vB//OMfHW0CZV9XVlaia9euWLZsWZ2Pu7OPU1NTsWnTJmzcuBF79uyB0WhESkoKbDabt1ajwVytd1VVFTIzM/HKK68gMzMTn3/+Oc6cOYPhw4fXajtlyhSn42DlypXe6H6j1be/gfqPbX/c30D96/77dS4oKMCHH34IlUqFxx57zKmd7Pe5B35MnhqpV69e4tlnn3Vadvvtt4s5c+b4qEfSKyoqEgDE7t27HcsmTZokRowY4btOSWDevHmia9eudT5mt9uFXq8Xb731lmNZTU2N0Ol0YsWKFV7qoXfMmDFDtG/fXtjtdiFEYO5rIYQAIDZt2uS4784+LisrE2q1WmzcuNHRJj8/XwQFBYktW7Z4re834/r1rsv+/fsFAJGTk+NY1q9fPzFjxgxpOyehuta7vmM7EPa3EO7t8xEjRoiBAwc6LfOHfc4zRD5iNptx6NAhJCcnOy1PTk7G3r17fdQr6RkMBgBAVFSU0/Jdu3YhNjYWHTp0wJQpU1BUVOSL7nnU2bNnER8fj4SEBIwdOxbnz58HAGRnZ6OwsNBp32u1WvTr1y+g9r3ZbMb69evx1FNPQaVSOZYH4r6+njv7+NChQ7BYLE5t4uPjkZSUFFDHgcFggEqlqvXj2B9//DFiYmLQuXNnzJo1y+/PjgKuj22l7O/Lly/j66+/xuTJk2s9Jvd9rpif7pCbq1evwmazIS4uzml5XFwcCgsLfdQraQkhMHPmTNx3331ISkpyLB82bBj++Mc/om3btsjOzsYrr7yCgQMH4tChQ357CfjevXvjo48+QocOHXD58mXMnz8fffv2xYkTJxz7t659n5OT44vuSmLz5s0oKyvDE0884VgWiPu6Lu7s48LCQmg0GjRv3rxWm0D5N6CmpgZz5szBuHHjnH79fPz48UhISIBer0dWVhbmzp2Lo0eP1voBbn9S37GthP0NAGvXrkVkZCRGjRrltNwf9jkLIh/7/V/OwG9Fw/XLAsVzzz2HY8eOYc+ePU7Lx4wZ4/j/pKQk9OzZE23btsXXX39da1D5i2HDhjn+v0uXLujTpw/at2+PtWvXOr5oGej7ftWqVRg2bBji4+MdywJxX7vSmH0cKMeBxWLB2LFjYbfb8cEHHzg9NmXKFMf/JyUlITExET179kRmZia6d+/u7a56RGOP7UDZ39d8+OGHGD9+PEJDQ52W+8M+50dmPhITE4Pg4OBafxkUFRXV+qsyEEyfPh1ffvkldu7ciVatWrls27JlS7Rt2xZnz571Uu+kFxERgS5duuDs2bOO2WaBvO9zcnKwfft2PP300y7bBeK+BuDWPtbr9TCbzSgtLb1hG39lsVgwevRoZGdnIyMjw+nsUF26d+8OtVodUMfB9cd2IO/va3744QecPn263nEPyHOfsyDyEY1Ggx49etQ6XZiRkYG+ffv6qFeeJ4TAc889h88//xw7duxAQkJCvc8pLi5Gbm4uWrZs6YUeeofJZMLJkyfRsmVLx2nj3+97s9mM3bt3B8y+X716NWJjY/Hwww+7bBeI+xqAW/u4R48eUKvVTm0KCgqQlZXl18fBtWLo7Nmz2L59O6Kjo+t9zokTJ2CxWALqOLj+2A7U/f17q1atQo8ePdC1a9d628pyn/vwC92Kt3HjRqFWq8WqVavEL7/8IlJTU0VERIS4cOGCr7vmMX/+85+FTqcTu3btEgUFBY5bVVWVEEKIiooKkZaWJvbu3Suys7PFzp07RZ8+fcQtt9wiysvLfdz7xktLSxO7du0S58+fF/v27RMpKSkiMjLSsW/feustodPpxOeffy6OHz8uHn/8cdGyZUu/XudrbDabaNOmjZg9e7bT8kDb1xUVFeLw4cPi8OHDAoBYvHixOHz4sGM2lTv7+NlnnxWtWrUS27dvF5mZmWLgwIGia9euwmq1+mq16uVqvS0Wixg+fLho1aqVOHLkiNOYN5lMQgghzp07J1577TVx4MABkZ2dLb7++mtx++23i27duvntert7bPvj/hai/mNdCCEMBoMIDw8Xy5cvr/V8f9nnLIh87B//+Ido27at0Gg0onv37k7T0QMBgDpvq1evFkIIUVVVJZKTk0WLFi2EWq0Wbdq0EZMmTRIXL170bcdv0pgxY0TLli2FWq0W8fHxYtSoUeLEiROOx+12u5g3b57Q6/VCq9WKBx54QBw/ftyHPfacrVu3CgDi9OnTTssDbV/v3LmzzmN70qRJQgj39nF1dbV47rnnRFRUlAgLCxMpKSmy3x6u1js7O/uGY37nzp1CCCEuXrwoHnjgAREVFSU0Go1o3769eP7550VxcbFvV6wertbb3WPbH/e3EPUf60IIsXLlShEWFibKyspqPd9f9rlKCCEkPQVFREREJHP8DhEREREpHgsiIiIiUjwWRERERKR4LIiIiIhI8VgQERERkeKxICIiIiLFY0FEREREiseCiIiIiBSPBREREREpHgsiIiIiUjwWRERERKR4/w+WAtr9mMITygAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# Define classifier\n", "classifier = mapclassify.UserDefined(y=travel_times, bins=break_values)\n", "\n", "# Plot histogram for public transport rush hour travel time\n", "grid[\"pt_r_t\"].plot.hist(bins=50, title=\"User defined classes\", color=\"lightgray\")\n", "\n", "# Add vertical lines for class breaks\n", "for break_point in classifier.bins:\n", " plt.axvline(break_point, linestyle=\"dashed\", linewidth=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_**Figure 6.68**. Histogram of the travel time values with user defined class breaks. Data source: Tenkanen & Toivonen 2020._" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When plotting the map, we can pass the break values using `classification_kwds`. " ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create one subplot. Control figure size in here.\n", "fig, ax = plt.subplots(figsize=(6, 4))\n", "\n", "grid.plot(\n", " ax=ax,\n", " column=\"pt_r_t\",\n", " linewidth=0,\n", " scheme=\"UserDefined\",\n", " classification_kwds={\"bins\": break_values},\n", " legend=True,\n", " legend_kwds={\"title\": \"Travel times (min)\", \"bbox_to_anchor\": (1.4, 1)},\n", ")\n", "\n", "# Set the x and y axis off and adjust padding around the subplot\n", "plt.axis(\"off\")\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_**Figure 6.69**. Static map of travel times by car and public transport using a custom classification scheme. Data source: Tenkanen & Toivonen 2020._" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "#### Question 6.14\n", "\n", "Select another column from the data (for example, travel times by car: `car_r_t`) and visualize a thematic map using our custom classification scheme. " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "remove_cell" ] }, "outputs": [], "source": [ "# Use this cell to enter your solution." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "editable": true, "scrolled": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Solution\n", "\n", "# Create one subplot. Control figure size in here.\n", "fig, ax = plt.subplots(figsize=(6, 4))\n", "\n", "# Visualize the travel times using a classification scheme and add a legend\n", "grid.plot(\n", " ax=ax,\n", " column=\"car_r_t\",\n", " linewidth=0,\n", " scheme=\"UserDefined\",\n", " classification_kwds={\"bins\": break_values},\n", " legend=True,\n", " legend_kwds={\"title\": \"Travel times (min)\", \"bbox_to_anchor\": (1.4, 1)},\n", ")\n", "\n", "# Set the x and y axis off and adjust padding around the subplot\n", "plt.axis(\"off\")\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rule-based classification\n", "\n", "Sometimes our analysis task benefits from combining multiple criteria for classifying data. For example, we might want to find out locations that are outside the city center within a reasonable public transport travel time. Such a selection could help us classify the statistical grid squares based on the potential for finding apartments with good public transport connections while avoiding the most expensive areas in the city center.\n", "\n", "To implement this, we can use conditional statements to find grid squares where public transport travel time (column `pt_r_tt`) is less than a selected threshold value in minutes, and where walking distance (`walk_d`) is more than a selected threshold value in meters. Each rule will give a binary result (`True`/`False`) and we can further combine these rules to find those locations that meet both requirements." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "# Threhsold values\n", "pt_maximum = 30\n", "walk_minimum = 2500" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", " ... \n", "13226 False\n", "13227 False\n", "13228 False\n", "13229 False\n", "13230 False\n", "Name: pt_r_tt, Length: 13231, dtype: bool" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid[\"pt_r_tt\"] < pt_maximum" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 True\n", "1 True\n", "2 True\n", "3 True\n", "4 True\n", " ... \n", "13226 True\n", "13227 True\n", "13228 False\n", "13229 True\n", "13230 True\n", "Name: walk_d, Length: 13231, dtype: bool" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid[\"walk_d\"] > walk_minimum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then use our `pandas` skills to combine these rules. Notice that you need parentheses around each set of condition." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "grid[\"rule1\"] = (grid[\"pt_r_tt\"] < pt_maximum) & (grid[\"walk_d\"] > walk_minimum)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, now that we have our rule-based classification stored in one of our `GeoDataFrame`columns, we can use this information to visualize the areas that meet our criteria:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid.loc[grid[\"rule1\"] == True].explore()" ] }, { "cell_type": "raw", "metadata": { "editable": true, "raw_mimetype": "", "slideshow": { "slide_type": "" }, "tags": [ "hide-cell" ] }, "source": [ "% This cell is only needed to produce a figure for display in the hard copy of the book.\n", "\\adjustimage{max size={0.9\\linewidth}{0.9\\paperheight}, caption={\\emph{\\textbf{Figure 6.70}. Grid squares that meet the selection criteria.}}, center, nofloat}{../img/figure_6-70.png}\n", "{ \\hspace*{\\fill} \\\\}" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "_**Figure 6.70**. Grid squares that meet the selection criteria._" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "#### Question 6.15\n", "\n", "Change the threshold values above and see how the map changes!" ] }, { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "## Footnotes\n", "\n", "[^pysal]: \n", "[^mapclassify]: \n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.9" } }, "nbformat": 4, "nbformat_minor": 4 }