{ "cells": [ { "cell_type": "markdown", "id": "f5384e51", "metadata": {}, "source": [ "# Exercise 02: Analysing Bias and Inequalities in OSM\n", "\n", "## Motivation\n", "Administrative or \"official\" geodatasets usually try to reach the same level of coverage and quality for an entire country or federal state. In contrast, the coverage of OSM is not the same in any place of the world. There are places with a large active OSM community and others where nothing has been mapped at all. Without going deeper into the topic of data quality, the spatial (and temporal) distribution of mapping around the globe can already tell us many interesting properties of OSM. By analysing the spatial temporal footprint of mapping activity in OSM we can identify regions with stronger local communities and can also find out how mapping is organized on larger scales.\n", "\n", "> Overall, mapping in OSM was strongly biased towards regions with very high Human Development Index. However, humanitarian mapping efforts had a different footprint, predominantly focused on regions with medium and low human development. Despite these efforts, regions with low and medium human development only accounted for 28% of the buildings and 16% of the roads mapped in OSM although they were home to 46% of the global population. {cite}`Herfort2021`\n", "\n", "This variability in mapping does not only become visible in the overall spatial coverage of OSM, but also when investigating how much individual users are contributing to the map.\n", "\n", ">\"Not all users contribute equally to the map. OSM is no exception to the 90-9-1 rule found in online communities where only a small number of active contributors account for most of the contributions. By our calculations for OSM, the top 1.4% of editors are responsible for 90% of all the map changes.\" {cite}`Anderson2019`\n", "\n", ">\"The results show that only 38% (192,000) of the registered members carried out at least one edit in the OSM database and that only 5% (24,000) of all members actively contributed to the project in a more productive way.\" {cite}`Neis2012`\n", "\n", "```{figure} ../figs/anderson_et_al_corporate_editors.png\n", "---\n", "height: 350px\n", "name: jennings-map-corporate-mappers\n", "---\n", "Places where corporate editors are editing. (source: {cite}`Anderson2019`)\n", "```\n", "\n", "\n", "## How biased is the global distribution of OSM contributions?\n", "\n", "This exercise will investigate the global spatial distribution of mapping in OSM for 5 days in 2020-02. We will use the changesets from OSM as they allow us to distinguish mapping by user, specific changeset comments (often used to identify a certain mapping campaign or group) and contain the rough locations of the map changes. To get an overview, analysing the changesets is often a quick and relatively simple approach. To get a first idea of OSM's global footprint you don't need to start with taking a look at every single element, which might get too complicated very soon. So let's start with something simpler.\n", "\n", "### 1. Download OSM Changesets\n", "On [HeiBOX](https://heibox.uni-heidelberg.de/f/87fe1933837e4e5491fd/) you can download an geopackage file. This file contains the OSM changesets for the time of 2020-02-01 to 2020-02-06. We'll work on this smaller extract to make sure that computations don't take too long.\n", "\n", "```{note}\n", "In principle, it's of course also interesting to take a look at all 100,000,000+ changesets contributed so far to OSM. For this you could set up your own database containing all OSM changesets. You will find an overview on how to do it [here](https://github.com/ToeBee/ChangesetMD). Since this process took several hours for me, it might be easier for you to get started with the geopackage provided for download.\n", "```\n", "\n", "Also make sure to get two csv files we are going to use later:\n", "* facebook_users.csv\n", "* apple_users.csv\n", "\n", "### 2. Get an overview\n", "Connect to the geopackage in QGIS and load the Polygon layer to get an overview on the spatial coverage.\n", "\n", "````{dropdown} Show the result in QGIS.\n", "```{figure} ../figs/changesets_qgis.png\n", "---\n", "height: 350px\n", "name: changesets-qgis\n", "---\n", "Global distribution of OSM changesets bounding boxes for 2020-02-01 to 2020-02-06.\n", "```\n", "````\n", "\n", "Create a heatmap using the changeset centroid as an approximation for mapping location. You could create the heatmap in python as well. However, it might be nicer and will look more professional if you do this in QGIS. In QGIS you can add a background layer and others things such as grids easily. When doing it in python can give you a quick and dirty overview (e.g. with a scatterplot and setting the opacity / alpha value of the points).\n", "\n", "````{dropdown} Show the steps in QGIS.\n", "```{figure} ../figs/changesets_heatmap_qgis.png\n", "---\n", "height: 350px\n", "name: changesets-heatmap-qgis\n", "---\n", "To calculate the heatmap we use a radius of 5 degree and set the cell size to 0.5 degree. Note that these parameters are rather arbitrary and we use them here just to get a quick overview. For a more regional analysis you might want to use a metrical projection.\n", "```\n", "````\n", "\n", "### 3. Analyse contribution inequality\n", "We will run the following part of the analysis in Python. Some parts you might be able to also run with QGIS, but let's take this opportunity to practice our Python skills.\n", "\n", "#### Load the geopackage\n", "For this exercise we can directly use the centroids." ] }, { "cell_type": "code", "execution_count": 6, "id": "ab74f969", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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iduser_idcreated_atclosed_atnum_changesuser_namecommentsourcecreated_bygeometry
080396743103685832020-02-01 00:00:02+00:002020-02-01 00:00:04+00:00196mapsandplantsAligned buildings in VarešMapbox SatelliteJOSM/1.5 (15492 en)POINT (18.32850 44.16627)
180396744212892020-02-01 00:00:09+00:002020-02-01 00:00:10+00:008hogrodAlton,IL update 28NoneiD 2.17.1POINT (-90.08601 38.88162)
28039674516587352020-02-01 00:00:11+00:002020-02-01 00:00:13+00:0040rustywagonAdded houses.BingJOSM/1.5 (15628 en)POINT (-113.96775 53.53355)
3803967463075202020-02-01 00:00:13+00:002020-02-01 00:00:13+00:001wolfgang8741reclassify to lake and link to wikidataNoneiD 2.17.1POINT (-85.86732 44.74097)
48039674794581682020-02-01 00:00:18+00:002020-02-01 00:00:19+00:0027Gorka115added buildings, roads and/or detailsNoneiD 2.17.1POINT (16.90061 -21.96159)
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" ], "text/plain": [ " id user_id created_at closed_at \\\n", "0 80396743 10368583 2020-02-01 00:00:02+00:00 2020-02-01 00:00:04+00:00 \n", "1 80396744 21289 2020-02-01 00:00:09+00:00 2020-02-01 00:00:10+00:00 \n", "2 80396745 1658735 2020-02-01 00:00:11+00:00 2020-02-01 00:00:13+00:00 \n", "3 80396746 307520 2020-02-01 00:00:13+00:00 2020-02-01 00:00:13+00:00 \n", "4 80396747 9458168 2020-02-01 00:00:18+00:00 2020-02-01 00:00:19+00:00 \n", "\n", " num_changes user_name comment \\\n", "0 196 mapsandplants Aligned buildings in Vareš \n", "1 8 hogrod Alton,IL update 28 \n", "2 40 rustywagon Added houses. \n", "3 1 wolfgang8741 reclassify to lake and link to wikidata \n", "4 27 Gorka115 added buildings, roads and/or details \n", "\n", " source created_by geometry \n", "0 Mapbox Satellite JOSM/1.5 (15492 en) POINT (18.32850 44.16627) \n", "1 None iD 2.17.1 POINT (-90.08601 38.88162) \n", "2 Bing JOSM/1.5 (15628 en) POINT (-113.96775 53.53355) \n", "3 None iD 2.17.1 POINT (-85.86732 44.74097) \n", "4 None iD 2.17.1 POINT (16.90061 -21.96159) " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import geopandas as gpd\n", "\n", "try:\n", " df = gpd.read_file(\"changesets.gpkg\", layer='changesets_centroids')\n", " display(df.head())\n", "except:\n", " print(\"Make sure that you download the geopackage file.\")" ] }, { "cell_type": "markdown", "id": "8c111a83", "metadata": {}, "source": [ "#### How unequal are contributions distributed among users\n", "Basically we want to investigate to what extend the 90-9-1 rule applies to our dataset. First, count the number of changesets and the overall number of changes. In a second step, count the number of distinct users editing OSM data between 2020-02-01 to 2020-02-06." ] }, { "cell_type": "code", "execution_count": 2, "id": "7ad747fd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of changesets: 267378\n", "number of changes: 20208464\n", "number of unique users: 15328\n" ] } ], "source": [ "# count number of changesets\n", "changesets_count = len(df)\n", "print(f\"number of changesets: {changesets_count}\")\n", "\n", "# count number of changes\n", "changes_sum = df[\"num_changes\"].sum()\n", "print(f\"number of changes: {changes_sum}\")\n", "\n", "# count unique users\n", "unique_users_count = df[\"user_id\"].nunique()\n", "print(f\"number of unique users: {unique_users_count}\")" ] }, { "cell_type": "markdown", "id": "609ceddf", "metadata": {}, "source": [ "Calculate the number of changesets per user and the overall number of changes per user." ] }, { "cell_type": "code", "execution_count": 4, "id": "3d61e0d3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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user_idnum_changes
user_id
956058810
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.........
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15328 rows × 2 columns

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" ], "text/plain": [ " user_id num_changes\n", "user_id \n", "9560588 1 0\n", "6407181 6 0\n", "10646969 10 0\n", "6402052 60 0\n", "7994908 1 0\n", "... ... ...\n", "2913188 47 112353\n", "7589857 18 117452\n", "10253243 231 165520\n", "8065782 46 311081\n", "408769 96 918517\n", "\n", "[15328 rows x 2 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_users = df.groupby(\"user_id\").agg({\n", " \"user_id\": 'count', # the number of changesets per user\n", " \"num_changes\": 'sum' # the overall number of changes per user\n", "})\n", "\n", "# ensure your arr is sorted from lowest to highest values first\n", "df_users.sort_values(\n", " \"num_changes\", # sort by the overall number of changes\n", " inplace=True, # change existing dataframe\n", " ascending=True # show users with lowest activity at the top\n", ")\n", "\n", "display(df_users)" ] }, { "cell_type": "markdown", "id": "b6722c73", "metadata": {}, "source": [ "Finally, calculate Gini coefficient and Lorenz curve to get first insights into contribution inequality. When calculating this in python you can check [this Github page](https://gist.github.com/CMCDragonkai/c79b9a0883e31b327c88bfadb8b06fc4) and use the functions provided there. Make sure to sort your dataframe before you create the Lorenz curve." ] }, { "cell_type": "code", "execution_count": 8, "id": "6310beb9", "metadata": {}, "outputs": [], "source": [ "# Check this page on github:\n", "# - https://gist.github.com/CMCDragonkai/c79b9a0883e31b327c88bfadb8b06fc4\n", "# We copied the code from there.\n", "import numpy as np\n", "\n", "def gini(arr):\n", " count = arr.size\n", " coefficient = 2 / count\n", " indexes = np.arange(1, count + 1)\n", " weighted_sum = (indexes * arr).sum()\n", " total = arr.sum()\n", " constant = (count + 1) / count\n", " return coefficient * weighted_sum / total - constant\n", "\n", "def lorenz(arr):\n", " # this divides the prefix sum by the total sum\n", " # this ensures all the values are between 0 and 1.0\n", " scaled_prefix_sum = arr.cumsum() / arr.sum()\n", " # this prepends the 0 value (because 0% of all people have 0% of all wealth)\n", " return np.insert(scaled_prefix_sum, 0, 0)" ] }, { "cell_type": "code", "execution_count": 11, "id": "b2bce81b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Gini coefficient: 0.8818204045488676\n" ] }, { "data": { "image/png": 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M12bp0qWZXpeuXbsC//6+3H///dSqVYuePXtSsWJF7rzzzkxzh7J7rIvx8fHhpZdeYsmSJURERNC+fXumTp1KdHT0JfeTjDTnRvKEh4dHlvWGYWT7GFcyzyMrF1sM7r8TQfNbXrRNTo6XPmlx7Nix9OjRI8tts/vhnx3/bc8XXniB8ePHc+eddzJ58mRKly6N3W7noYceyjQ580oNHjyY0aNHc/jwYVJSUvjll19444038uz4sbGxdOjQgeDgYJ599lmqV6+Or68vmzZt4vHHH8/wfKZNm8bQoUP5+uuvWb58OQ8++CBTpkzhl19+oWLFirhcLmw2G0uWLMnyNbzUPK/cyO5iiHn9/sztOR544AHmzJnDQw89ROvWrQkJCcFms3HzzTdn+b7Jy7hdLhfdunXjsccey/Lx9C9OZcuWZfPmzSxbtowlS5awZMkS5syZw+DBg/nggw9ydKxLeeihh+jduzeLFi1i2bJljB8/nilTprBq1SqaNm2a4+dXEim5KWHCw8MJDg7mr7/+uuR2VapUyXKNlfRu+CpVquT43Hm58uzRo0dJSEjI0Huzc+dOAPeEu/RvZ7GxsRn2/e8315zElv68L9Y2YWFhmXqUClr6t3svLy/3t8WLqVKlCrt27cpUn9XzK1WqVKa2TE1N5dixYxnqFixYQKdOnXj//fcz1MfGxhIWFpadp5DBpV6bm2++mTFjxvDZZ5+RlJSEl5cXgwYNyvaxd+3aRadOndzlc+fOcezYMXr16gWYV9adOnWKhQsXZlhrad++fVker2HDhjRs2JCnn36adevW0bZtW2bOnMlzzz1H9erVMQyDqlWrXvIDLv09tmvXLjp37uyudzgc7Nu3j8aNG1/yOVWpUgWXy8WuXbvcPa0AMTExxMbG5up3NyvVq1dn2bJlnD59Olu9N5ezYMEChgwZwrRp09x1ycnJmd5z2ZX+PHfv3p2hV/jUqVOZeqWqV6/OuXPnLvv7AuYQeO/evenduzcul4v777+fd955h/Hjx1OjRo1sH+tyf3OqV6/OI488wiOPPMKuXbto0qQJ06ZN4+OPP75sjKJhqRLHbrfTt29f/u///i/LyzXTv/X06tWLDRs2ZLicNiEhgVmzZhEVFUW9evVyfO6AgIA8G5ZIS0vjnXfecZdTU1N55513CA8Pp1mzZgDuMf4Lx7idTiezZs3KdWzlypWjSZMmfPDBBxn+6P71118sX77c/aFopbJly9KxY0feeeedTIkHkOFy2V69evHLL7+wYcOGDI9fONSTrnr16pnmC8yaNStTz42Hh0emb8/z5893z/PJqYCAgIt+wIWFhdGzZ08+/vhjPvnkE6699tocJVCzZs3C4XC4y2+//TZpaWn07NkT+Ld34MLnk5qayltvvZXhOHFxcaSlpWWoa9iwIXa73X35df/+/fHw8GDSpEmZ2scwDE6dOgVA8+bNCQ8PZ+bMmRmWZpg7d262PujT34P/XZRy+vTpAFx33XWXPUZ23HjjjRiGwaRJkzI9lpvek6zeN6+//nque1q7dOmCp6cnb7/9dob6rHr2brrpJtavX8+yZcsyPRYbG+t+bdNfo3R2u919hVP665zdY6Vf/fXf1zQxMTHT1VzVq1cnKCgo06X8cnHquSmBXnjhBZYvX06HDh245557qFu3LseOHWP+/PmsXbuW0NBQnnjiCT777DN69uzJgw8+SOnSpfnggw/Yt28fX375pXvCaE40a9aMefPmMWbMGK6++moCAwPp3bt3rp5D+fLleemll9i/fz+1atVi3rx5bN68mVmzZrkniNavX59WrVoxbtw497fLzz//PNOHUE5je/nll+nZsyetW7fmrrvucl8KHhISkuU6MFZ48803ueaaa2jYsCF333031apVIyYmhvXr13P48GH3mjOPPfYYH330Eddeey2jR492XwpepUoV/vzzzwzHHD58OPfeey833ngj3bp1Y8uWLSxbtixTMnH99dfz7LPPMmzYMNq0acPWrVv55JNPsjVfJCvNmjXj+++/Z/r06ZQvX56qVavSsmVL9+ODBw9mwIABgLmcQE6kpqbSpUsXbrrpJnbs2MFbb73FNddcQ58+fQBo06YNpUqVYsiQITz44IPYbDY++uijTB/Cq1atYtSoUQwcOJBatWqRlpbGRx99hIeHBzfeeCNgfkA999xzjBs3jv3799O3b1+CgoLYt28fX331Fffccw9jx47Fy8uL5557jhEjRtC5c2cGDRrEvn37mDNnTrbasHHjxgwZMoRZs2a5h9U2bNjABx98QN++fTP0VF2JTp06cccdd/Daa6+xa9curr32WlwuFz/99BOdOnXK8f2krr/+ej766CNCQkKoV68e69ev5/vvv3dfKp5TERERjB49mmnTptGnTx+uvfZatmzZwpIlSwgLC8vQc/Loo4/yzTffcP3117svg09ISGDr1q0sWLCA/fv3ExYWxvDhwzl9+jSdO3emYsWKHDhwgNdff50mTZq4e8myeyw/Pz/q1avHvHnzqFWrFqVLl6ZBgwakpaW535P16tXD09OTr776ipiYGG6++eZctUWJVNCXZ0nhcODAAWPw4MFGeHi44ePjY1SrVs0YOXJkhktP9+zZYwwYMMAIDQ01fH19jRYtWhjffvtthuOkX1b930tgs7rM99y5c8att95qhIaGGoD7MueLHePCx/57KXj9+vWN33//3WjdurXh6+trVKlSxXjjjTcy7b9nzx6ja9euho+PjxEREWE8+eSTxooVKzId82KxZfU8DMMwvv/+e6Nt27aGn5+fERwcbPTu3dv4559/MmyTflnziRMnMtRf7BL1/7rYpeBZXSJKFpeU7tmzxxg8eLARGRlpeHl5GRUqVDCuv/56Y8GCBRm2+/PPP40OHToYvr6+RoUKFYzJkycb77//fqYYnU6n8fjjjxthYWGGv7+/0aNHD2P37t1ZXgr+yCOPGOXKlTP8/PyMtm3bGuvXrzc6dOiQ4TLm7F4Kvn37dqN9+/aGn59fpkt4DcNcBqBUqVJGSEiIkZSUdMk2TZf+Gvzwww/GPffcY5QqVcoIDAw0brvttgyX+BuGYfz8889Gq1atDD8/P6N8+fLGY489ZixbtizDe2jv3r3GnXfeaVSvXt3w9fU1SpcubXTq1Mn4/vvvM537yy+/NK655hojICDACAgIMOrUqWOMHDnS2LFjR4bt3nrrLaNq1aqGj4+P0bx5c+PHH3/M1IYX43A4jEmTJhlVq1Y1vLy8jEqVKhnjxo3LsDSAYZiXgl933XWZ9s/uedLS0oyXX37ZqFOnjuHt7W2Eh4cbPXv2NDZu3OjeBjBGjhyZad//vm/OnDljDBs2zAgLCzMCAwONHj16GNu3b8+0Xfpr99+lLLL6W5GWlmaMHz/eiIyMNPz8/IzOnTsb27ZtM8qUKWPce++9GfaPj483xo0bZ9SoUcPw9vY2wsLCjDZt2hivvPKKe7mABQsWGN27dzfKli1reHt7G5UrVzZGjBhhHDt2LMfHMgzDWLdundGsWTPD29vb/Tt88uRJY+TIkUadOnWMgIAAIyQkxGjZsqXxxRdfXPb1kH/ZDCMPZ42JSLEwd+5chg0bxr59+wr9omFpaWmUL1+e3r17Z5rnI/JfsbGxlCpViueee46nnnrK6nAkn2jOjYgUaYsWLeLEiRMMHjzY6lCkkPnvTVjh37lIHTt2LNhgpEBpzo2IFEm//vorf/75J5MnT6Zp06Z06NDB6pCkkJk3bx5z586lV69eBAYGsnbtWj777DO6d+9O27ZtrQ5P8pGSGxEpkt5++20+/vhjmjRpkunGpiJgroTs6enJ1KlTiYuLc08yfu6556wOTfKZ5tyIiIhIsaI5NyIiIlKsKLkRERGRYqXEzblxuVwcPXqUoKCgPL0dgIiIiOQfwzCIj4+nfPnyl11ItsQlN0ePHqVSpUpWhyEiIiK5cOjQISpWrHjJbUpcchMUFASYjRMcHJynx3Y4HCxfvpzu3bu7bwEgeU/tXDDUzgVD7Vxw1NYFI7/aOS4ujkqVKrk/xy+lxCU36UNRwcHB+ZLc+Pv7ExwcrF+cfKR2Lhhq54Khdi44auuCkd/tnJ0pJZpQLCIiIsWKkhsREREpVpTciIiISLGi5EZERESKFSU3IiIiUqwouREREZFiRcmNiIiIFCtKbkRERKRYUXIjIiIixYqSGxERESlWLE1ufvzxR3r37k358uWx2WwsWrTosvusWbOGq666Ch8fH2rUqMHcuXPzPU4REREpOixNbhISEmjcuDFvvvlmtrbft28f1113HZ06dWLz5s089NBDDB8+nGXLluVzpCIiIlJUWHrjzJ49e9KzZ89sbz9z5kyqVq3KtGnTAKhbty5r167lf//7Hz169MivMEVERCQbklKdxO5YS0pinKVxFKm7gq9fv56uXbtmqOvRowcPPfTQRfdJSUkhJSXFXY6LMxvc4XDgcDjyNL704+X1cSUjtXPBUDsXDLVzwVFb5zPDxYFvXqLG1v9R2d4QR2q/PD18Tl63IpXcREdHExERkaEuIiKCuLg4kpKS8PPzy7TPlClTmDRpUqb65cuX4+/vny9xrlixIl+OKxmpnQuG2rlgqJ0Ljto673k74rjq4CzqxP0JNkjAn5XLl+Cye+fZORITE7O9bZFKbnJj3LhxjBkzxl2Oi4ujUqVKdO/eneDg4Dw9l8PhYMWKFXTr1g0vL688Pbb8S+1cMNTOBUPtXHDU1vnDdnAdHl89hu1cNE67D0+l3MGvfh35rkeXPG3n9JGX7ChSyU1kZCQxMTEZ6mJiYggODs6y1wbAx8cHHx+fTPVeXl759ubOz2PLv9TOBUPtXDDUzgVHbZ1HXE74aTqseQEMF5Spyfqmr/D5twlUtRl53s45OVaRWuemdevWrFy5MkPdihUraN26tUURiYiIlEDnjsPH/WH1c2Zi0+hmuGcN50JrA2CzODxLk5tz586xefNmNm/eDJiXem/evJmDBw8C5pDS4MGD3dvfe++97N27l8cee4zt27fz1ltv8cUXX/Dwww9bEb6IiEjJs/cHmHkN7F0Dnn5ww1vQ/x3wCcQwrA7OZOmw1O+//06nTp3c5fS5MUOGDGHu3LkcO3bMnegAVK1ale+++46HH36YV199lYoVK/Lee+/pMnAREZH85nLCDy/BD1MBA8LrwsC5ULaOe5P03MbqnhtLk5uOHTtiXCLNy2r14Y4dO/LHH3/kY1QiIiKSQdwxWHg37P/JLDe9HXq+DN4ZrzpWz42IiIgUfrtXwsJ7IPEkeAXA9f+DxoMuuYvN4q4bJTciIiKSmTPNvBLqp+mAARENzGGosJoX3cWgcHTdKLkRERGRjM4egS/vgoPrzXLzO6HHC+CV9bIr6f4dlrI2yVFyIyIiIv/auRy+GgFJp8E7CPq8Cg1uzNaumlAsIiIihYfTASufhXWvmeVyjWHAHChT3dq4ckHJjYiISEkXexAW3AmHfzPLLUZA98ngmXmF/0u51BXQBUnJjYiISEm2/TtYdD8kx4JPCNzwBtTrc0WH1NVSIiIiUvDSUmHFBPj1bbNc/ioYOAdKReX6kIWk40bJjYiISIlzeh8sGAZHzy+K22okdH0GPL0tDSuvKLkREREpSf75Gr4eBSlx4BsKfd+GOr3y5NDp69zoaikRERHJf45kWP40/PauWa7YAgbMhtBKeXaK9GEpJTciIiKSv07tgflDIfpPs9x2NHQeDx5eloaVX5TciIiIFGdbF8D/PQSp8eBfBvq9AzW75cupNKFYRERE8o8jCZY+ARvnmuXKbWDA+xBcPt9O6V6hWJeCi4iISJ46sdMchjr+N2CD9mOhwxPgkb8f+06XCwAPJTciIiKSZ7Z8Dt+OAUcCBIRD/1lQvXOBnNrhNPtu7EpuRERE5IqlJsDix2Dzx2Y5qh3c+B4ERRZYCE6Xmdyo50ZERESuzPFt5jDUie2ADTo+Ae0fBbtHgYaR5lLPjYiIiFwJw4A/PobFj0JaEgRGmL01VdtbEk76nBslNyIiIpJzKefguzHw5zyzXK0T9H8XAsMtC8k958ayCExKbkRERIqa6L9g/hA4tRtsduj0FFwzBuzWphWacyMiIiI5YxiwcQ4seQKcKRBU3ly7pkobqyMD/p1zo+RGRERELi85Dv5vNPy90CzX7A59Z0JAGWvjuoDm3IiIiEj2HN0MC4bB6b1g94QuE6D1A5YPQ/2XrpYSERGRSzMM2PAuLH8KnKkQUsm8k3elFlZHlqU0LeInIiIiF5UUC988ANu+Mcu1e8ENb4J/aUvDuhRNKBYREZGsHd4IC4ZC7EGwe0G3Z6HVfdbfkfIy0txzbqy9PbiSGxERkcLCMOCXt2DFRHA5ILQKDJwDFZpZHVm2qOdGRERE/pV4Gr4eCTsWm+W6faDP6+AXamlYOaEbZ4qIiIjp0AaYPwziDoOHN/R4Aa4eXuiHof7LqaulRERESjiXC9a9BiufBcMJpavBwLlQrrHVkeWKFvETEREpyRJOwVcjYPcKs9zgRrh+BvgGWxrWldAifiIiIiXVgXWw4C6IPwqevnDti9BsaJEbhvqv9HVu1HMjIiJSUrhcsHYarH4BDBeUqWkOQ0U2sDqyPOFwqudGRESk5Dh3HBbeA3tXm+VGN8N108An0Nq48lDq+eTGU8mNiIhIMbfvR/hyOJyLAU8/uO4VaHJbkR+G+q/UtPPJjcW3vFJyIyIikl9cTvjxZfjhJXMYKryOOQxVtq7VkeULd3KjnhsREZFiKD7a7K3Z/5NZbno79HwZvP2tjSsfpZ6fUKyeGxERkeJmzypzfk3CCfAKgOunQ+ObrY4q36WmOQHw1L2lREREiglnGqyZAj9NAwyIaAAD5kB4LasjKxDuCcXquRERESkGzh4xh6EOrjPLzYbBtVPAy8/auApQ+pwbrXMjIiJS1O1cbq42nHQavIOg9wxoOMDqqAqcrpYSEREp6pwO875Q614zy5GNzKuhylS3NCyrpN8VXFdLiYiIFEWxh2DBnXB4g1lucQ90mwxevtbGZSH3sJR6bkRERIqY7Yth0X2QHAs+IXDD61DvBqujspRhGFqhWEREpMhJS4Xvn4Ff3jTL5a+CAbOhdFVLwyoM0hMb0JwbERGRouHMfpg/DI5uMsut7oeuk8DT29KwCov0+TagnhsREZHC759v4OtRkHIWfEOh79tQp5fVURUq6fNtQD03IiIihZcjGVaMhw2zzHLFFjDgfQitbG1chdC/k4lt2NVzIyIiUgid2gPzh0L0n2a57WjoPB48vCwNq7BKT268rV7BDyU3IiIimf31JXwzGlLjwa809HsHanW3OqpCLdVp3lfK2+oxKZTciIiI/MuRBEvHwcY5Zrlya7jxfQipYG1cRUBqmjmh2MvqRW5QciMiImI6ucschor5C7BBu0eg4zjw0EdldqRfCu6t5EZERKQQ2DIPvn0YHAngHwY3vgvVO1sdVZGS4jCHpXw0LCUiImKh1ERY8ij88bFZjmoHN74HQZHWxlUEJZ1Pbvy8PSyORMmNiIiUVMe3w/whcGI7YIMOj0OHx8Bu/YdzUZScntx4Wd9+lvcdvfnmm0RFReHr60vLli3ZsGHDJbefMWMGtWvXxs/Pj0qVKvHwww+TnJxcQNGKiEiRZxhmT82sjmZiExgBg7+GTuOU2FyBxFQzufEtBMmNpT038+bNY8yYMcycOZOWLVsyY8YMevTowY4dOyhbtmym7T/99FOeeOIJZs+eTZs2bdi5cydDhw7FZrMxffp0C56BiIgUJR7OZDz+byRs/cKsqNYJ+s+CwMyfOZIz7mEpL8v7TaztuZk+fTp33303w4YNo169esycORN/f39mz56d5fbr1q2jbdu23HrrrURFRdG9e3duueWWy/b2iIiIEPM3HXZMxL71C7DZzQX5bl+oxCaPJKnnBlJTU9m4cSPjxo1z19ntdrp27cr69euz3KdNmzZ8/PHHbNiwgRYtWrB3714WL17MHXfccdHzpKSkkJKS4i7HxcUB4HA4cDgcefRscB/zwv8lf6idC4bauWConQuAYWD740M8VzxFUFoyrsByuPrNwqjcGpxO859csYRk8z3sc/6umfn1GZsdliU3J0+exOl0EhERkaE+IiKC7du3Z7nPrbfeysmTJ7nmmmswDIO0tDTuvfdennzyyYueZ8qUKUyaNClT/fLly/H397+yJ3ERK1asyJfjSkZq54Khdi4Yauf84elMovHBOVSM/QWAmOBGbKoygtS/zsBfiy2Ornj566AdsHPi2BGomvfv6cTExGxvW6SullqzZg0vvPACb731Fi1btmT37t2MHj2ayZMnM378+Cz3GTduHGPGjHGX4+LiqFSpEt27dyc4ODhP43M4HKxYsYJu3brh5aV7j+QXtXPBUDsXDLVzPor+E8+Fd2GL3Ydh88DR/kl+OVudbt17qK3zwR+Lt8ORg9SqHgWuvXn+nk4feckOy5KbsLAwPDw8iImJyVAfExNDZGTW6wuMHz+eO+64g+HDhwPQsGFDEhISuOeee3jqqaew2zNPIfLx8cHHxydTvZeXV769ufPz2PIvtXPBUDsXDLVzHjIM+O09WPYkOFMhuCK2gXOwRTaFxYvV1vkk5fzoXoCPFyTl/Xs6J8eybEKxt7c3zZo1Y+XKle46l8vFypUrad26dZb7JCYmZkpgPDzMiUuGYeRfsCIiUjQkxZpr1yweayY2tXvBvT9BpRZWR1bsJWsRP9OYMWMYMmQIzZs3p0WLFsyYMYOEhASGDRsGwODBg6lQoQJTpkwBoHfv3kyfPp2mTZu6h6XGjx9P79693UmOiIiUUEc2wvxhEHsA7F7QbRK0uh9sNqsjKxESU9OAEn61FMCgQYM4ceIEEyZMIDo6miZNmrB06VL3JOODBw9m6Kl5+umnsdlsPP300xw5coTw8HB69+7N888/b9VTEBERqxkG/PI2rJgALgeEVoYBc6FiM6sjK1GSHOaNMwvDOjeWTygeNWoUo0aNyvKxNWvWZCh7enoyceJEJk6cWACRiYhIoZd4Gr4eBTu+M8t1e0OfN8Av1NKwSqLk1H9vv2D1RBHLkxsREZFcOfQbLBgGZw+Bhzf0eAGuHq5hKItceOPM7F+0nT+U3IiISNHicsH612Hls+BKg1JVYeBcKN/E6shKtKQLbpyp5EZERCS7Ek7Bovtg1zKzXL8/9H4VfPN23TLJOd1+QUREJKcOrIMFd0H8UfDwgZ4vQbOhGoYqJOLP334h0Mf61ML6CERERC7F5YK102H1C2A4oUwNGPgBRDawOjI5zzAMEs733AT6qOdGRETk4s6dgK/ugT2rzHKjQXDddPAJtDYuySDZ4cLpMq+RUs+NiIjIxez7Cb4cDueiwdMPer0MTW/XMFQhFJ9iDknZbOBf0lcoFhERycTlhB9fhh9eAsMF4XXMq6HK1rU6MrmIc8nm6sSB3p7YCkHyqeRGREQKj/gYWDgc9v1olpvcDr2mgneAtXHJJZ1LOZ/c+BaOtKJwRCEiIrJnNSy8GxJOgFcAXD8dGt9sdVSSDe6em0Iw3waU3IiIiNWcabBmCvw0DTCgbH1zGCq8ltWRSTap50ZERCRd3FFz0vCBn81ys6Fw7Yvg5WdpWJIz7uRGPTciIlKi7VoBX42AxFPgHWiuNNxwgNVRSS4ouRERkZLN6YBVk+HnV81yZCNzGKpMdUvDktyL15wbEREpsWIPwZd3waFfzfLVd0P358DL19q45Ipozo2IiJRMO5bAV/dCciz4BEOf16F+X6ujkjyQfrVUkHpuRESkREhLhZWTYP0bZrl8UxgwB0pXtTYuyTPquRERkZLjzH5YcCcc2WiWW90PXSeBp7elYUneSp9zE6CeGxERKdb++Qa+HgUpZ8E3BPq+DXWuszoqyQdxSea9pUL8vCyOxKTkRkRE8lZaCix/GjbMMssVr4YBsyG0srVxSb45ez65CfUrHD1ySm5ERCTvnNoDC4bBsS1muc2D0GUCeBSOb/SSP2KTUgH13IiISHHz10L45kFIjQe/0tBvJtTqYXVUUgDcPTf+Sm5ERKQ4cCTBsifh99lmuXJruPF9CKlgbVxSIJIdTpIdLgCC1XMjIiJF3sldMH8oxPwF2KDdGOj4JHjo46WkSJ9MbLeZ69w4nWkWR6TkRkREcuvPL+D/HgJHAviHQf9ZUKOL1VFJAUsfkgr288Jut+F0WhwQSm5ERCSnUhNhyWPwx0dmOaod9H8XgstZG5dYItZ9pVThGJICJTciIpITx7ebw1AntgE26PA4dHgM7B5WRyYWOZtYuNa4ASU3IiKSXX98AovHgiMRAiPM3ppqHayOSiyW3nMT4l841rgBJTciInI5KefMpGbLZ2a5WkczsQksa2lYUjicLWSrE4OSGxERuZSYv81hqJM7wWaHTk/CNY+A3W51ZFJInE00F/DTnBsRESncDAM2fQBLHoe0ZAgqZ65dE9XW6sikkClsC/iBkhsREfmvlHjzEu+/FpjlGl2h3zsQEGZpWFI4xWpYSkRECrVjW8xhqNN7weZh3heqzYMahpKLOnXOHJYqE6gJxSIiUpgYBvz2Hix7CpwpEFzRvJN35ZZWRyaF3MlzKQCUCfCxOJJ/KbkRESnpks/CNw/AP1+b5Vo9oe9b4F/a2rikSDiVoJ4bEREpTI5sMoehYg+A3Qu6TYJW94PNZnVkUgS4XAanzyc3YYHquRERESsZBvw6E5aPB5cDQivDgLlQsZnVkUkRcjbJgdNlAFBKi/iJiIhlks7A16Ng+7dmuW5v6PMG+IVaGpYUPacSzPk2IX5eeHsWnknnSm5EREqSQ7/Bgjvh7EHw8Ibuz0OLuzUMJblyshBeKQVKbkRESgaXC9a/ASsngSsNSlWFgXOgfFOrI5MiLH2+TZkAJTciIlKQEk/DV/fCrmVmuX4/6P0a+AZbG5cUeacK4WXgoORGRKR4O7AevrwL4o6Ahw/0fBGaDdMwlOQJDUuJiEjBcbng5//BqufBcEKZGjBwLkQ2tDoyKUbSJxSXKUSXgYOSGxGR4ufcCfhqBOxZaZYb3gTXTwefIGvjkmIn/dYLYeq5ERGRfLN/LSy4C85Fg6cf9HoZmt6uYSjJF+77ShX1OTdJSUkYhoG/vz8ABw4c4KuvvqJevXp07949zwMUEZFscDnhx1fghxfBcEFYbXMYKqKe1ZFJMXYifUJxIeu5yfGKOzfccAMffvghALGxsbRs2ZJp06Zxww038Pbbb+d5gCIichnxMfBRX1jzgpnYNLkd7lmtxEbylWEYRJ9NBiAy2NfiaDLKcXKzadMm2rVrB8CCBQuIiIjgwIEDfPjhh7z22mt5HqCIiFzCntUw8xrY9yN4+UO/d6Dvm+AdYHVkUszFp6SR5HACEFHIkpscD0slJiYSFGROSlu+fDn9+/fHbrfTqlUrDhw4kOcBiohIFpxp5hDUj68ABpStbw5DhdeyOjIpIWLO99oE+3ri5+1hcTQZ5bjnpkaNGixatIhDhw6xbNky9zyb48ePExysBaFERPJd3FH4sA/8+DJgwFVD4O6VSmykQEXHnR+SCilcvTaQi+RmwoQJjB07lqioKFq0aEHr1q0BsxenaVMt4y0ikq92fW8OQx34GbwD4cb3oc9r4OVndWRSwsTEmZOJC9uQFORiWGrAgAFcc801HDt2jMaNG7vru3TpQr9+/fI0OBEROc/pgNXPw9r/meXIhjDwAyhT3dq4pMSKOd9zUyySG4DIyEjOnTvHihUraN++PX5+flx99dXYtI6CiEjeO3vYvJP3oV/N8tXDzbt5exW+DxUpOf5NbgrXGjeQi+Tm1KlT3HTTTaxevRqbzcauXbuoVq0ad911F6VKlWLatGn5EaeISMm0YyksuheSzoBPMPR5Her3tToqkUJ7GTjkYs7Nww8/jJeXFwcPHnQv5AcwaNAgli5dmqfBiYiUWGmpsOwp+GyQmdiUbwojflRiI4VGTLw556ZsIUxuctxzs3z5cpYtW0bFihUz1NesWVOXgouI5IUzB2DBMDiy0Sy3uh+6PgOeha/7X0qumOLUc5OQkJChxybd6dOn8fHJ+S/em2++SVRUFL6+vrRs2ZINGzZccvvY2FhGjhxJuXLl8PHxoVatWixevDjH5xURKZS2/R+8085MbHxD4OZP4dopSmykUHG6DPetFwrjhOIcJzft2rVz334BwGaz4XK5mDp1Kp06dcrRsebNm8eYMWOYOHEimzZtonHjxvTo0YPjx49nuX1qairdunVj//79LFiwgB07dvDuu+9SoUKFnD4NEZHCJS0FFj8G826H5LNQ8Wq4dy3Uuc7qyEQyOXUuBafLwG4rfHcEh1wMS02dOpUuXbrw+++/k5qaymOPPcbff//N6dOn+fnnn3N0rOnTp3P33XczbNgwAGbOnMl3333H7NmzeeKJJzJtP3v2bE6fPs26devw8vICICoqKqdPQUSkUPFPicHjg14QvcWsaPMAdJkIHl7WBiZyEUfPD0mVDfLF0yPH/ST5LsfJTYMGDdi5cydvvPEGQUFBnDt3jv79+7uHirIrNTWVjRs3Mm7cOHed3W6na9eurF+/Pst9vvnmG1q3bs3IkSP5+uuvCQ8P59Zbb+Xxxx/HwyPrpZ9TUlJISUlxl+Pi4gBwOBw4HI5sx5sd6cfL6+NKRmrngqF2LhiurQvpuH0CdlcShl9pnL3fwKjZHVyAS22fl/SezjsHT8YDUD7UN1N75lc75+R4uVrnJiQkhKeeeio3u7qdPHkSp9NJREREhvqIiAi2b9+e5T579+5l1apV3HbbbSxevJjdu3dz//3343A4mDhxYpb7TJkyhUmTJmWqX758eZZzh/LCihUr8uW4kpHauWConfOH3ZVKgyOfUvXkKgBOBdTk96iRJO9Kg12aR5if9J6+cquP2gAPSDh90Xmved3OiYmJ2d42x8nNn3/+mWW9zWbD19eXypUr52picXa4XC7Kli3LrFmz8PDwoFmzZhw5coSXX375osnNuHHjGDNmjLscFxdHpUqV6N69e57fC8vhcLBixQq6devmHjaTvKd2Lhhq53x0ajeeC4djO/kXADsjelPxjrfo7KNbKOQnvafzzsbvtsOBgzSrW41ePTLe0yy/2jl95CU7cpzcNGnSxL0SsWEYABlWJvby8mLQoEG88847+PpefAZ1WFgYHh4exMTEZKiPiYkhMjIyy33KlSuHl5dXhiGounXrEh0dTWpqKt7emSc1+fj4ZJlseXl55dubOz+PLf9SOxcMtXMe+3M+fPsQpJ4D/zDS+rzFth3JVPXxUzsXEL2nr1z0+ftKVS4TcNG2zOt2zsmxcjwL6KuvvqJmzZrMmjWLLVu2sGXLFmbNmkXt2rX59NNPef/991m1ahVPP/30JY/j7e1Ns2bNWLlypbvO5XKxcuVK9804/6tt27bs3r0bl8vlrtu5cyflypXLMrERESk0UhPhmwdg4XAzsYlqB/euxaje2erIRHLs6NkkAMqHFs7exhz33Dz//PO8+uqr9OjRw13XsGFDKlasyPjx49mwYQMBAQE88sgjvPLKK5c81pgxYxgyZAjNmzenRYsWzJgxg4SEBPfVU4MHD6ZChQpMmTIFgPvuu4833niD0aNH88ADD7Br1y5eeOEFHnzwwZw+DRGRgnNiB8wfCsf/AWzQ4THo8DjYPUCTW6UIOnKmmCU3W7dupUqVKpnqq1SpwtatWwFz6OrYsWOXPdagQYM4ceIEEyZMIDo6miZNmrB06VL3JOODBw9it//buVSpUiWWLVvGww8/TKNGjahQoQKjR4/m8ccfz+nTEBEpGJs/he8eAUciBJSFG9+Dah2sjkok1xJT0ziTaCblFUoVk+SmTp06vPjii8yaNcs9FORwOHjxxRepU6cOAEeOHMl0FdTFjBo1ilGjRmX52Jo1azLVtW7dml9++SWnYYuIFKzUBDOp2fKZWa7WEfq/C4FlLQ1L5EodjTV7bYJ8PAn2LZxzl3Kc3Lz55pv06dOHihUr0qhRI8DszXE6nXz77beAecn2/fffn7eRiogUFTF/m8NQJ3eCzQ4dn4R2Y8xhKJEi7kisuYBfYR2SglwkN23atGHfvn188skn7Ny5E4CBAwdy6623EhQUBMAdd9yRt1GKiBQFhgGbPoQlj0FaMgSVM4ehoq6xOjKRPJPec1NYh6Qgl4v4BQUFce+99+Z1LCIiRVdKPHz7MGydb5ZrdIV+70BAmLVxieSxfycTF74bZqbLVXKza9cuVq9ezfHjxzNclg0wYcKEPAlMRKTIOPanOQx1eg/YPKDLeGgzGuyF7547IlfqSGzhvlIKcpHcvPvuu9x3332EhYURGRmZYQE/m82m5EZESg7DgN/fh6VPgjMFgivAgNlQuZXVkYnkmwOnEgCoXDp/bmGUF3Kc3Dz33HM8//zzuvxaREq25LPwzYPwzyKzXOta6Ps2+Je2NCyR/HbwtHmPpyqlAyyO5OJynNycOXOGgQMH5kcsIiJFw5FNsGAYnNkPdk/oOglaj4QLerJFiqNzKWmcPJcKQOUyhbfnJscDwgMHDmT58uX5EYuISOFmGPDLTHi/u5nYhFSGO5dBm1FKbKREOHjK7LUp5e9FiF/hXOMGctFzU6NGDcaPH88vv/xCw4YNM93ISrdCEJFiKekMfD0KtpvreVHnerjhDfArZW1cIgXo4Onz823KFN4hKchFcjNr1iwCAwP54Ycf+OGHHzI8ZrPZlNyISPFz+HeYPwzOHgQPb+j+HLS4R701UuIcOJU+36bwDklBLpKbffv25UccIiKFj2HA+jfg+2fAlQalomDgXCjf1OLARKyxPz25KcTzbSCX69yIiBR7iadh0X2wc6lZrt8Per8KviHWxiViIfewVHHruQE4fPgw33zzDQcPHiQ1NTXDY9OnT8+TwERELHPwF1hwJ8QdAQ8fuHYKNL9Tw1BS4rmHpYrbnJuVK1fSp08fqlWrxvbt22nQoAH79+/HMAyuuuqq/IhRRKRguFzw8wxY9RwYTihd3RyGKtfI6shELJfscLpXJ44KK9w9Nzm+FHzcuHGMHTuWrVu34uvry5dffsmhQ4fo0KGD1r8RkaIr4SR8OhBWTjITm4YDYcQPSmxEztt3MgHDgGBfT8IDfawO55JynNxs27aNwYMHA+Dp6UlSUhKBgYE8++yzvPTSS3keoIhIvtu/FmZeA7u/B08/6PM69H8XfIKsjkyk0Nh9/BwANcoGZrj1UmGU4+QmICDAPc+mXLly7Nmzx/3YyZMn8y4yEZH85nLCD1Phg94QfwzCasPdq+CqwZpfI/Ife06YyU318ECLI7m8HM+5adWqFWvXrqVu3br06tWLRx55hK1bt7Jw4UJatdLN4kSkiIiPgYV3w77z63U1uQ16vQzehXuipIhVLuy5KexynNxMnz6dc+fMJzhp0iTOnTvHvHnzqFmzpq6UEpGiYe8a+PJuSDgOXv5w3XRocovVUYkUantOmJeBF8vkplq1au6fAwICmDlzZp4GJCKSb1xO+OElcygKA8rWM6+GCq9tdWQihZrTZbC3OA9LpUtNTeX48eO4XK4M9ZUrV77ioERE8lzcMfhyOBxYa5avGgI9XwIvP2vjEikCjpxJIiXNhbennUqFfAE/yEVys3PnTu666y7WrVuXod4wDGw2G06nM8+CExHJE7u/h4X3QOIp8A6E62dAIy1dIZJd6ZOJq4UF4GEv/JPtc5zcDBs2DE9PT7799lvKlStX6C8HE5ESzJkGq5+Dtf8zyxENzWGosBqWhiVS1KRPJi4KQ1KQi+Rm8+bNbNy4kTp16uRHPCIieePsYVhwFxz6xSxfPRy6Pw9evtbGJVIEuS8DLwKTiSEXyU29evW0no2IFG47l8FXIyDpDPgEQ5/XzBtfikiu/NtzUzSWSshWchMXF+f++aWXXuKxxx7jhRdeoGHDhnh5eWXYNjg4OG8jFBHJLqcDvn8G1r9hlss1gYFzoHS1S+0lIpdgGAY7YuIBqBVRNFbtzlZyExoammFujWEYdOnSJcM2mlAsIpY6c8C8k/eR381yy/ug2yTwLNz3wBEp7I6eTSY+OQ1Pu614zblZvXp1fschIpJ7276Fr++H5LPgGwI3vAV1r7c6KpFiYUe0OXpTPTwQb88c37XJEtlKbjp06JDfcYiI5FxaCqyYCL++bZYrNIcBs6FUFWvjEilGtkebQ1K1I4vGkBTkYkLxnDlzCAwMZODAjGtEzJ8/n8TERIYMGZJnwYmIXNTpfTB/KBzbbJZbj4IuE8HT28qoRIqdHUUwuclx/9KUKVMICwvLVF+2bFleeOGFPAlKROSS/l4E77Q3Exu/UnDLPOjxvBIbkXyw/ZiZ3NQpQslNjntuDh48SNWqVTPVV6lShYMHD+ZJUCIiWXIkw7In4ff3zXKlVjDgfQipaG1cIsVUaprLvcZNnXJF52roHPfclC1blj///DNT/ZYtWyhTpkyeBCUiksmpPfB+138Tm2vGwNBvldiI5KO9J8+R5jII8vWkfEjRWQAzxz03t9xyCw8++CBBQUG0b98egB9++IHRo0dz880353mAIiJsXQD/NxpSz4F/GPR/B2p0tToqkWLPPd8mIqhI3W4px8nN5MmT2b9/P126dMHT09zd5XIxePBgzbkRkbzlSIIlj8GmD81ylWvgxvcguJy1cYmUENuOFb3JxJCL5Mbb25t58+bx3HPPsXnzZvz8/GjYsCFVqujSSxHJQyd2mFdDHf8HsEGHx6D9Y+CR4z9bIpJL6WvcFKX5NpCL5CZdzZo1qVmzZl7GIiJi2vwZfDcGHIkQUBZufBeqdbQ6KpESJ31YqihdKQVXkNyIiOS51ARY/Chs/sQsV+0A/d+FoAhr4xIpgU6dS+Ho2WRAyY2ISO7E/GMOQ53cATY7dBwH7R4Bu4fVkYmUSFuPnAWgWlgAQb5el9m6cFFyIyLWMgz44yNY/BikJUFQOXPScNQ1VkcmUqL9dT65aVAhxOJIck7JjYhYJyUevh0DW78wy9W7QP9ZEJB5FXQRKVjpPTeNKha95CZXt/f86aefuP3222ndujVHjhwB4KOPPmLt2rV5GpyIFGPRW2FWRzOxsXmY94W6bYESG5FC4q8j5pVSRbHnJsfJzZdffkmPHj3w8/Pjjz/+ICUlBYCzZ89qnRsRuTzDgN/eh3e7wKndEFwBhi2GdmPAnqvvWyKSx06dS+FIbBIA9csXrcvAIRfJzXPPPcfMmTN599138fL6d4JR27Zt2bRpU54GJyLFTPJZWDDMvMzbmQK1roV710LlVlZHJiIXKMqTiSEXc2527Njhvu3ChUJCQoiNjc2LmESkODr6B8wfBmf2gd0Tuk6C1iOhCC3pLlJSFOXJxJCLnpvIyEh2796dqX7t2rVUq1YtT4ISkWLEMODXd+D97mZiE1IZ7lwGbUYpsREppIryZGLIRc/N3XffzejRo5k9ezY2m42jR4+yfv16xo4dy/jx4/MjRhEpqpLOwNejYPu3ZrnO9XDDG+BXytq4ROSSth4u2j03OU5unnjiCVwuF126dCExMZH27dvj4+PD2LFjeeCBB/IjRhEpig5vhAVDIfYg2L2g+3PQcoR6a0QKueNxyRw9m4zNVjQnE0MOkxun08nPP//MyJEjefTRR9m9ezfnzp2jXr16BAYG5leMIlKUGAasfxO+nwiuNCgVBQPmQIWrrI5MRLJh08FYAGpHBBXJycSQw+TGw8OD7t27s23bNkJDQ6lXr15+xSUiRVHiaVh0P+xcYpbr9YU+r4Fv0ezaFimJ/jh0BoCmlYvu8HGOJxQ3aNCAvXv35kcsIlKUHfwVZrYzExsPH7huGgycq8RGpIj543zPTdPKoZbGcSVytc7N2LFj+fbbbzl27BhxcXEZ/olICeNywdr/wZyeEHcYSleH4d/D1cM1v0akiHE4Xfx5OBaAq4pwcpPjCcW9evUCoE+fPtgu+MNlGAY2mw2n05l30YlI4ZZwEr4aAbu/N8sNB8L1/wOfIGvjEpFc2REdT7LDRbCvJ9XCiu5c2hwnN6tXr86POESkqNn/M3x5F8QfA09f6PUyNL1DvTUiRdimg+Z8myaVS2G3F93f5RwnNx06dMiPOESkqHA54afpsOYFMFwQVgsGfgARusBApKhLn29TlIekIBfJTbrExEQOHjxIampqhvpGjRpdcVAiUkidOw4L74a9a8xy41vhulfAO8DSsEQkb6T33BTlK6UgFxOKT5w4wfXXX09QUBD169enadOmGf7lxptvvklUVBS+vr60bNmSDRs2ZGu/zz//HJvNRt++fXN1XhHJgb0/wNttzcTGyx/6vg393lZiI1JMRJ9N5sCpROy2ot9zk+Pk5qGHHiI2NpZff/0VPz8/li5dygcffEDNmjX55ptvchzAvHnzGDNmDBMnTmTTpk00btyYHj16cPz48Uvut3//fsaOHUu7du1yfE4RyQGXE1a/AB/eAAnHoWw9uHs1NLnV6shEJA/9uu8UYN5yoagu3pcux8nNqlWrmD59Os2bN8dut1OlShVuv/12pk6dypQpU3IcwPTp07n77rsZNmwY9erVY+bMmfj7+zN79uyL7uN0OrntttuYNGmSbtYpko98HWfw+LQ//PASYMBVg2H4Sihbx+rQRCSP/bLXTG5aVi1tcSRXLsfJTUJCAmXLlgWgVKlSnDhxAoCGDRuyadOmHB0rNTWVjRs30rVr138Dstvp2rUr69evv+h+zz77LGXLluWuu+7Kafgikk22vavpuP1p7Ad+Bu9A6P8e9HkdvP2tDk1E8sGve08D0LJqGYsjuXI5nlBcu3ZtduzYQVRUFI0bN+add94hKiqKmTNnUq5cuRwd6+TJkzidTiIiIjLUR0REsH379iz3Wbt2Le+//z6bN2/O1jlSUlJISUlxl9MXGnQ4HDgcjhzFeznpx8vr40pGaud85krD/sOLeK6bgSfgKlsfZ//3oUwNUJvnOb2fC47a+uKOx6ew92QCNhs0rRh0RW2UX+2ck+PlOLkZPXo0x44dA2DixIlce+21fPLJJ3h7ezN37tycHi5H4uPjueOOO3j33XcJCwvL1j5Tpkxh0qRJmeqXL1+Ov3/+fANdsWJFvhxXMlI75z3f1NM03/8WZRJ2ArAvrDN/lbsV1687gZ3WBlfM6f1ccNTWmW06aQM8KO9nsHZ13rRPXrdzYmJitre1GYZhXOnJtm/fTuXKlbOdcKRLTU3F39+fBQsWZLjiaciQIcTGxvL1119n2H7z5s00bdoUDw8Pd53L5QLM4awdO3ZQvXr1DPtk1XNTqVIlTp48SXBw3t7K3eFwsGLFCrp164aXV9GejFWYqZ3zh23Xcjz+bxS2pNMYPkGk9niFpYf81M75TO/ngqO2vrgJ3/zDZ78dZmjryjzV68rm1OVXO8fFxREWFsbZs2cv+/md63Vu0vn7+3PVVVflal9vb2+aNWvGypUr3cmNy+Vi5cqVjBo1KtP2derUYevWrRnqnn76aeLj43n11VepVKlSpn18fHzw8fHJVO/l5ZVvb+78PLb8S+2cR5wOWDkJ1r1ulss1wTZwDvagSnBosdq5gKidC47aOrPfDsQC0LpGeJ61TV63c06OlePkxul0MnfuXFauXMnx48fdPSfpVq1alaPjjRkzhiFDhtC8eXNatGjBjBkzSEhIYNiwYQAMHjyYChUqMGXKFHx9fWnQoEGG/UNDQwEy1YtINsQehAV3wuHfzHLLe6Hbs+Dpo/k1IiXEsbNJ7D5+DruteFwpBbmcczN37lyuu+46GjRokOHmmbkxaNAgTpw4wYQJE4iOjqZJkyYsXbrUPcn44MGD2O05vqhLRC5n+3ew6D5IPgu+IXDDm1C3t9VRiUgB+2nnSQAaVQwl1N/b4mjyRo6Tm88//5wvvvjCfXfwvDBq1Kgsh6EA1qxZc8l983sSs0ixk5YKKybAr2+b5QrNYMAcKFXF2rhExBI/7jKXdGlfK9ziSPJOjpMbb29vatSokR+xiEh+O70PFgyDo3+Y5dajoMtE8Cwe39ZEJGecLoO1u82em/Y1c3ZRUGGW4/GeRx55hFdffZUrvMhKRAra34vgnfZmYuNXCm75HHo8r8RGpATbeuQssYkOgnw8aVIp1Opw8ky2em769++fobxq1SqWLFlC/fr1M81eXrhwYd5FJyJXzpEMy5+C394zy5VawoDZEFLR2rhExHI/7TSHpNrUKIOnR/GZ35qt5CYkJCRDuV+/fvkSjIjksVN7YP5QiP7TLF/zMHR6Cjx0GayIFM/5NpDN5GbOnDn5HYeI5LWtC+D/RkPqOfAvA/1mQc2ul99PREqE+GQHmw7GAtC+ZglMbi6UlJSEYRjuWxccOHCAr776inr16tG9e/c8D1BEcsiRBEufgI1zzXKVtnDjexBc3tKwRKRwWbfnFE6XQVQZfyqVLl43xM3xANsNN9zAhx9+CEBsbCwtWrRg2rRp3HDDDbz99tt5HqCI5MCJnfBul/OJjQ3aPwaDv1FiIyKZrNp2HICOtctaHEney3Fys2nTJtq1awfAggULiIyM5MCBA3z44Ye89tpreR6giGTT5s9gVgc4/jcElIU7voLOT4HHFd9lRUSKGZfLYOV2M7npUrf4JTc5/quXmJhIUFAQYN5Zu3///tjtdlq1asWBAwfyPEARuYzUBFj8KGz+xCxXbQ/934OgCGvjEpFC688jZzl5LoVAH09aVi1jdTh5Lsc9NzVq1GDRokUcOnSIZcuWuefZHD9+PM/vsi0il3F8G7zb2UxsbHbzSqg7FimxEZFLWrktBoD2tcLw9iw+l4Cny/EzmjBhAmPHjiUqKoqWLVvSunVrwOzFadq0aZ4HKCJZMAzY9BHM6gQntkNgpDm3psNjYPewOjoRKeRWnp9v07lO8fwilONhqQEDBnDNNddw7NgxGjdu7K7v0qWL1r8RKQgp5+Dbh2HrF2a5emfzMu/A4nUpp4jkj6OxSfxzLA6bDTrVLp5/N3I10zAyMpLIyMgMdS1atMiTgETkEqK3movyndoNNg/o/DS0fQjsxa9bWUTyR/pE4qsql6JMoI/F0eQPXUYhUhQYBmycA0ueAGcKBFeAG9+HKq2tjkxEipj0+TbF8SqpdEpuRAq75DhzpeG/z9+3rWYP6DcT/EtbG5eIFDlnkxz8fP4u4N3qFs/5NqDkRqRwO7rZHIY6sw/sntD1GWg1UsNQIpIr3/8Tg8NpULNsIDUjgqwOJ98ouREpjAwDNrxr3s3bmQohlc07eVe62urIRKQIW/LXMQB6NSxncST5S8mNSGGTFAvfjIJt/2eW61wPN7wBfqUsDUtEira4ZAc/7jSHpJTciEjBObwRFgyF2INg94Luz0HLEWCzWR2ZiBRxq7YdJ9Xponp4ALUiAq0OJ18puREpDAwDfnkLVkwElwNKRcGAOVDhKqsjE5Fi4rut/w5J2Yr5FyYlNyJWSzwNi+6HnUvMcr0boM/r4BtibVwiUmzEJzv4YecJoPgPSYGSGxFrHfwVFtwJcYfBwweufQGa36VhKBHJU6u2Hyc1zUXVsADqRBbfq6TSKbkRsYLLBeteg5XPguGE0tVh4Fwo18jqyESkGPpm81EAejWMLPZDUqDkRqTgJZyEr+6F3SvMcoMB0HsG+BT/b1MiUvBOnUtxD0n1a1rB4mgKhpIbkYJ0YJ05DBV/DDx9oedUuGqwhqFEJN98t/UYaS6DhhVCqFG2ZHyJUnIjUhBcLlg7DVa/AIYLwmqZw1AR9a2OTESKuYWbjgDQt4T02oCSG5H8d+44LLwH9q42y41vgV6vgE/xXmdCRKy372QCmw/F4mG30adxeavDKTBKbkTy094fYOHdcC4GvPzNpKbpbVZHJSIlxKI/zF6ba2qEER7kY3E0BUfJjUh+cDnhh6nww0uAAeF1zWGosnWsjkxESgjDMFi02Uxu+l9VcoakQMmNSN6Lj4Yvh8P+n8xy0zvMicPe/tbGJSIlyq/7TnPgVCIB3h50qxdhdTgFSsmNSF7avdKcX5N4ErwCzEu8G91kdVQiUgJ9tuEgADc0rYC/d8n6uC9Zz1YkvzjTYM0L8NN0wICIhuYwVFgNqyMTkRLodEIqS7ZGA3Bri8oWR1PwlNyIXKmzR8xhqIPrzHLzO6HHFPDytTYuESmxFm46TKrTRcMKITSoUPLuU6fkRuRK7FwOX42ApNPgHQR9XoMG/a2OSkRKMMMw+PT8kNQtJbDXBpTciOSO02HeF2rda2a5XGNzGKp0NUvDEhH5dd9p9p5IIMDbgz5NSs7aNhdSciOSU7GHzFsoHN5glluMgO6TwbPkrCEhIoXXh+v3A9CnSQUCfUrmx3zJfNYiubV9MSy6D5JjwScEbngD6vWxOioREQAOn0lk6V/mROKhbaKsDcZCSm5EsiMtFb6fCL+8ZZYrNIMBs6FUlKVhiYhc6KP1B3AZ0LZGGWpHloybZGZFyY3I5ZzZD/OHwdFNZrn1KOgyETy9LQ1LRORCialp7rVthrWpanE01lJyI3Ip/3wNXz8AKWfBNxT6zYTaPa2OSkQkky83HSEuOY0qZfzpXKes1eFYSsmNSFYcybD8afjtXbNcqSXc+D6EVrI2LhGRLLhcBnN+3geYc23sdpvFEVlLyY3If53aA/OHQvSfZrntQ9D5afDwsjIqEZGL+mHXCfaeSCDIx5OBzfUlTMmNyIW2LoD/ewhS48G/DPSbBTW7Wh2ViMglvb1mDwA3XV2pxF7+fSG1gAiAIwmWPgEb55rlKm3hxvcguGQugCUiRcfGA6fZsO80Xh42hrcr2ROJ0ym5ETm5yxyGivkLsEH7sdDhCfDQr4eIFH5vrTZ7bfo1rUC5ED+Loykc9NdbSrYt8+Dbh8GRAAHh0P9dqN7J6qhERLJle3QcK7cfx2aDER2qWx1OoaHkRkqm1ERY/Chs/tgsV21vJjZBkdbGJSKSAzPPz7Xp2SCS6uGBFkdTeCi5kZLn+DZzGOrEdrDZzSGo9mPB7mF1ZCIi2bb/ZAL/9+cxAO7vWMPiaAoXJTdSchgGbP4EvhsLaUkQGGlOGq7azurIRERy7NWVu3C6DDrWDqdBhRCrwylUlNxIyZByDr4bA3/OM8vVO5uXeQeGWxuXiEgu7IqJZ9HmIwA80q22xdEUPkpupPiL/sschjq1C2we0PkpaPsw2O1WRyYikiv/+34nhgE96kfQsKJ6bf5LyY0UX4Zhrluz5HFwpkBQefNO3lVaWx2ZiEiu/X30LIu3RmOzwcPdalkdTqGk5EaKp+Q4+PYh+OtLs1yzB/R9GwLKWBqWiMiVmrZ8JwC9G5WnTmSwxdEUTkpupPg5tsUchjq9F+ye0GUitB6lYSgRKfJ+3n2SVduP42m38VDXmlaHU2gpuZHiwzDgt/dg2ZPgTIWQSjBgDlS62urIRESumNNl8Nx32wC4vVUVqmldm4tSciPFQ1IsfPMAbPvGLNe+Dm54A/xLWxqWiEhe+XLjYbYdiyPY15PRXdRrcymFop/+zTffJCoqCl9fX1q2bMmGDRsuuu27775Lu3btKFWqFKVKlaJr166X3F5KgCMb4Z32ZmJj94JrX4SbP1FiIyLFRkJKGq8s3wHAA51rUirA2+KICjfLk5t58+YxZswYJk6cyKZNm2jcuDE9evTg+PHjWW6/Zs0abrnlFlavXs369eupVKkS3bt358iRIwUcuVjOMGD9W/B+D4g9AKFV4K5l0Oo+sNmsjk5EJM+8uXo3x+NTqFzan8FtqlgdTqFneXIzffp07r77boYNG0a9evWYOXMm/v7+zJ49O8vtP/nkE+6//36aNGlCnTp1eO+993C5XKxcubKAIxcreaWdw2P+HbBsHLgcUO8GuPcnqNDM6tBERPLUzph4Zv24F4CnrquLj6duFXM5liY3qampbNy4ka5du7rr7HY7Xbt2Zf369dk6RmJiIg6Hg9KlNQRRUtgO/0bH7eOx71oKHt7Q6xUY+AH4aiErESleDMPg6a/+Is1l0LVuWbrXi7A6pCLB0gnFJ0+exOl0EhGR8cWKiIhg+/bt2TrG448/Tvny5TMkSBdKSUkhJSXFXY6LiwPA4XDgcDhyGXnW0o+X18eV8wwX9l/exGPN8/i70nCFVsV54/sQ2QjS0qyOrtjR+7lgqJ0LTlFs6y83HWHD/tP4edl5uldt0orA37r8auecHK9IXy314osv8vnnn7NmzRp8fX2z3GbKlClMmjQpU/3y5cvx9/fPl7hWrFiRL8ctybzT4ml6YBaRcVsAOFyqFVsqDSNt02HgsLXBFXN6PxcMtXPBKSptneCA5zd7ADa6lXOwZd1qtlgdVA7kdTsnJiZme1tLk5uwsDA8PDyIiYnJUB8TE0NkZOQl933llVd48cUX+f7772nUqNFFtxs3bhxjxoxxl+Pi4tyTkIOD83ZlR4fDwYoVK+jWrRteXl55euySzHZwPR6LHscWfwzD05fULpPZGFOWbt27q53zkd7PBUPtXHCKWls/vvAvEtKOUrNsAC/e2RovD8unyWZLfrVz+shLdlia3Hh7e9OsWTNWrlxJ3759AdyTg0eNGnXR/aZOncrzzz/PsmXLaN68+SXP4ePjg4+PT6Z6Ly+vfHtz5+exSxSXC9ZOh9UvgOGEMjWxDZyLvUxtWLxY7VxA1M4FQ+1ccIpCW6/efpyFfxzFZoMp/Rvh75v5c6ywy+t2zsmxLB+WGjNmDEOGDKF58+a0aNGCGTNmkJCQwLBhwwAYPHgwFSpUYMqUKQC89NJLTJgwgU8//ZSoqCiio6MBCAwMJDBQqzUWG+dOwMK7Ye9qs9zoZrhuGvgEQhEaLxcRyamzSQ7GLdwKwLA2VWkepQtmcsry5GbQoEGcOHGCCRMmEB0dTZMmTVi6dKl7kvHBgwexX3BPoLfffpvU1FQGDBiQ4TgTJ07kmWeeKcjQJb/s+xG+HA7nYsDTz0xqmt5mdVQiIgXiuW//IToumaphATzao7bV4RRJlic3AKNGjbroMNSaNWsylPfv35//AYk1XE748WX44SUwXBBeFwbOhbJ1rI5MRKRArN5+nPkbD2OzwdQBjfDz1po2uVEokhsR4qPNYah9P5rlpndAz6ngnT9XtImIFDYn4lN4dMGfgDkcdbWGo3JNyY1Yb88qWHgPJJwArwDoPQMa3WR1VCIiBcblMhg7fwsnz6VQKyKQx67VcNSVUHIj1nGmwZop8NM0wICIBuYwVJjudisiJcvsn/fxw84T+HjaeePWq/D10nDUlVByI9Y4e8ScNHxwnVlufif0eAG8/KyNS0SkgG09fJaXlpqr8k/oXY9aEUEWR1T0KbmRgrdrhTkMlXQavIOgz6vQ4EaroxIRKXBnEx2M/HQTDqfBtfUjubVFZatDKhaU3EjBcTpg1WT4+VWzXK4xDJgDZapbG5eIiAVcLoPR8/7g4OlEKpby48UbG2Kz2awOq1hQciMFI/YQLLgTDm8wyy1GQPfJ4Fn0Vt0UEckLM77fyZod5jybmbc3I9Tf2+qQig0lN5L/ti+GRfdBciz4hMANb0C9PlZHJSJimRX/xPDaqt0AvHhjQxpUCLE4ouJFyY3kn7RU+P4Z+OVNs1z+Khg4B0pFWRmViIildsbEM2beZgCGtomiX9OK1gZUDCm5kfxxZr85DHVko1luPQq6TARPdbuKSMl1Ij6FYXN+Iz4ljRZRpXnqurpWh1QsKbmRvPfPN/D1KEg5C76h0G8m1O5pdVQiIpZKSnUy/MPfORKbRNWwAN65oxleHvbL7yg5puRG8k5aCix/GjbMMssVW8CA2RBaydq4REQs5nQZjPliM1sOxRLq78XsoVdTKkA92flFyY3kjVN7YMEwOLbFLLcdDZ3Hg4eXtXGJiFjMMAzGf/0XS/6KxtvDzqw7mlM1LMDqsIo1JTdy5f76Er4ZDanx4F8G+r0DNbtZHZWISKHw0tIdfPrrQWw2mD6oMS2q6oaY+U3JjeSeIwmWjoONc8xy5TYw4H0ILm9tXCIihcRba3Yz84c9AEzp15DrG+nvY0FQciO5c3IXzB8KMX8BNmg/Fjo8AR56S4mIAMz9eR9Tl+4A4KledblZt1YoMPokkpzbMg++fRgcCRAQDv1nQfXOVkclIlJovPfTXp77bhsAD3Suwd3tq1kcUcmi5EayLzURljwKf3xslqPawY3vQVCktXGJiBQib63Z7e6xGdWpBmO61bI4opJHyY1kz/Ht5jDUiW2ADTo+Ae0fBbuH1ZGJiBQKhmHw6spdzPh+FwAPd63F6K41LY6qZFJyI5f3xyfw3SOQlgSBEWZvTdX2VkclIlJoOF0GE77+i09+PQjAoz1qM7JTDYujKrmU3MjFpZyDxWNhy2dmuXpn6DcLAsOtjUtEpBBJSnXy4Od/sOKfGGw2eKZ3fYa0ibI6rBJNyY1kLeZvcxjq5E6w2aHTU3DNGLBrqXARkXSnE1K564Pf+ONgLN6edl67uQnXNihndVglnpIbycgwYNMHsORxSEuGoPLm2jVV2lgdmYhIobL7eDzDP/id/acSCfHz4r0hzbk6Sgv0FQZKbuRfyXHw7UPmisMANbtD35kQUMbSsERECptV22N48LPNnEtJo0KoH3OHXU3NiCCrw5LzlNyI6dgWcxjq9F6we0KXCdD6AQ1DiYhcwDAM3vlxLy8t3Y5hQIuqpXn7tqsoE+hjdWhyASU3JZ1hwG/vwbInwZkKIZXMO3lXamF1ZCIihcrZJAePLdjCsr9jALilRWUm9amPt6e+BBY2Sm5KsuSz8M0D8M/XZrl2L7jhTfDXmLGIyIW2HIpl1GebOHQ6CS8PGxOur8ftrapgs9msDk2yoOSmpDqyEeYPg9gDYPeCbs9Cq/tAv6giIm4ul8Hsn/fx0tLtOJwGlUr78cYtV9G4UqjVocklKLkpaQwDfp0Jy8eDywGhVWDgHKjQzOrIREQKlYOnEhm7YAsb9p0G4Nr6kbw0oBEhfl4WRyaXo+SmJEk8DV+Pgh3fmeW6faDP6+AXamlYIiKFiWEYfPzrQaYs3kZiqhN/bw+e7FWX21pW1jBUEaHkpqQ49BssGAZnD4GHN/R4Aa4ermEoEZEL7D4ez9OL/uKXvWZvTcuqpXl5QGMql/G3ODLJCSU3xZ3LBevfgJWTwJUGpavBwLlQrrHVkYmIFBqJqWm8tnI37/20lzSXga+XncevrcOQ1lHY7foSWNQouSnOEk7Bovtg1zKz3OBGuH4G+AZbGpaISGFhGAZL/4rmue+2cSQ2CYCudcsysXd9KpVWb01RpeSmuDqwHr68C+KOgKcvXPsiNBuqYSgRkfM2HjjDC4u3sfHAGQAqlvLjmd716VovwuLI5EopuSluXC74+X+w6nkwnFCmpjkMFdnA6shERAqFA6cSmb5yN4u3RgPg5+XB3e2rcV+H6vh5e1gcneQFJTfFybkT8NU9sGeVWW50M1w3DXwCrY1LRKQQOBqbxBd77WzY8DMOp4HdBgObVWJM91pEBPtaHZ7kISU3xcW+n+DL4XAuGjz94LpXoMltGoYSkRLv8JlE3lqzh/m/H8LhtAMGHWuH80TPOtSJ1BzE4kjJTVHncsKPr8APL4LhgvA65jBU2bpWRyYiYqndx+N5f+0+Fmw8jMNpAFAz2MUzA1vQtqbm1RRnSm6KsvgYWDgc9v1olpveDj1fBm/N8BeRkskwDNbtOcV7P+1l9Y4T7vq2NcowskM1TvyznhZRun9ecafkpqjasxoW3g0JJ8ArAK7/HzQeZHVUIiKWSExN49stx5j98z62R8cD5qh8t7oR3N2+GldHlcbhcLD4H4sDlQKh5KaocaaZQ1A/vgIYENEABsyB8FpWRyYiUuD+OnKWzzYc5OvNRzmXkgaYVz/d1Lwiw9pWJSoswOIIxQpKboqSuKPmpOEDP5vlZsPg2ing5WdtXCIiBSg2MZVv/zzGvN8OsfXIWXd9VBl/bmlRmZuvrkyIv25uWZIpuSkqdn1vXuadeAq8g6D3DGg4wOqoREQKRFKqk++3xfD15iP8sPOEe4Kwt4edaxtEcnOLSrSuVkY3thRAyU3h53TAqufg5xlmObKReTVUmepWRiUiku9S01ys23OSbzYfZdnf0SSkOt2P1SsXTP+rKtD/qoqUDvC2MEopjJTcFGaxh8xbKBz61Sy3uAe6TQYvLTYlIsVTXLKDNTtOsPzvaNbsOOGeRwPm7RFuaFKevk0qUDMiyMIopbBTclNY7Vhi3vQy6Qz4hMANr0O9G6yOSkQkTxmGwZ4TCfy06wSrth/nl72n3ENOAGWDfOhRP5K+TctzVeVSGnaSbFFyU9ikpcLKSbD+DbNc/ioYMBtKV7U2LhGRPHImIZWf95zkp50n+WnXCY6eTc7weI2ygXSrF0H3ehE0rhiK3a6ERnJGyU1hcmY/LLgTjmw0y61GQtdnwFPjySJSdJ1OSOW3/af5bd9pNuw/zdYjZzH+7ZzB29NOi6jStKsZRtd6EVQP1/3w5MoouSkstv0fLBoJKWfBNxT6vg11elkdlYhIjhiGwbGzyfy2/zQb9pn/dh0/l2m72hFBtKsZRrta4bSIKq27cUueUnJjtbQUWD4eNrxjliu2gAHvQ2hla+MSEcmGMwmpbDkcy5+Hz/Ln4Vi2HD7LifiUTNvVKBvI1VGlaVm1NK2qlSEyRBdGSP5RcmOl03th/jA4ttkstx0NnceDhxafEpHCxTAMTpxLYfuxeLZHx51PZs5y8HRipm097Dbqlw/m6qjS5/+VokygjwVRS0ml5MYqfy2Ebx6E1HjwKw393oFa3a2OSkSEpFQnO2Pi2REdz7boOHZEx7M9Op7TCalZbl81LIBGFUNoVDGUxhVDqF8+RMNMYiklNwXNkQzLxsHvs81y5dZw4/sQUsHauESkRHG5DI7EJrHvZAJ7T5wz/z+ZwN4TCRw9m5Rhwm86uw2iwgKoExlE/fIhZkJTIVS3OpBCR8lNQTq5G+YPhZitgA3aPQIdx4GHXgYRyXtnkxwcPpPI4TNJ5/+ZPx86nci+kwmkpLkuum9YoDe1I4OoExlM7cgg6kYGUzMiEF8v9chI4adP1YLy5xfwfw+BIwH8w+DGd6F6Z6ujEpEiyDAMzqWkEROXwvG4ZGLik4mJSyH6bLI7iTkSm0R8ctolj+PtYadKGX+qhgVQLTyQamEBVAsPoGpYgObISJGm5Ca/pSbCksfgj4/MclQ7uPE9CIq0Ni4RKXQSU9M4dS6V0wmpnE5M5fT5n0+cSyEmLpnos8kcjzd/TrzgPkuXEhboTYVS/lQs5Xf+n/lz9bBAKpTyw0ML5EkxpOQmPx3fbg5DndgG2KDjE9D+UbCrW1ekuDIMgySHk7NJDuKS0ohLdhCX5CAu2cHZRAdxyWmcTXJwOiGVUwmpnDqXzNGTHjz++/ckOy4+TJSVIF9PIoJ9iQj2ISLIl7LBvlQ4n8RUKuVHhVB/TeyVEqlQJDdvvvkmL7/8MtHR0TRu3JjXX3+dFi1aXHT7+fPnM378ePbv30/NmjV56aWX6NWrkC1498cnsHgsOBIhMMLsrana3uqoRCQLDqeLxBQnCalpJKamkZD+s7vOSULK+f8vrE9xci7lwgQmjbgkB2muLGbjXpINMBMbb087ZQK8KX3Bv/BAHyKCfSkb7ENksK/7Z3/vQvEnXKTQsfw3Y968eYwZM4aZM2fSsmVLZsyYQY8ePdixYwdly5bNtP26deu45ZZbmDJlCtdffz2ffvopffv2ZdOmTTRo0MCCZ5CRhzMZj29GwtZ5ZkW1TtB/FgRmfi4iJZHTZeBwukhzGTjSXDhcLhxOgzSn+b/D6SLNaZDqdJF2frvU83VpTpf7Z4fTRUqai2SHk5Q0FylpTpId5v8pDhfJaS5Szj+WfMH/qWmuTHU5T0Yuz9NuI9jPi2BfT4L9vAjx8yLY14tgP0+Cfb3ciUuIrwfbNv/G9d06EhEaQIC3h24OKXKFLE9upk+fzt13382wYcMAmDlzJt999x2zZ8/miSeeyLT9q6++yrXXXsujjz4KwOTJk1mxYgVvvPEGM2fOLNDYL5SS5uTknk1cs30i9tRjGDY7Z1s9RnzzByDVDv9Z6Oq/l1kaGJd47D/l/2yQ+fGLP3r5Y+cgrktsm9XjV7LvhcW0tDT2x8PmQ7F4enpe9jlkpw0MA1yGgWGYsRgGGX52GecjPF/ncqXvZ/z7v2HWuS742cjimJmO9Z9z/XvMyxzrgnNx/nHXBedwGQZOl4HTMHC5DJwusqi74GeDDHVpTifRMXbmn9iIgZmUuFzgPH8M97Eu+NllkKHO4TRIc7nOJzFmcpIPeUSe8faw4+/jQYC3J/7eHvj7eBLg7YG/tycBPuf/v7D+/P8hfl7nExkzeQnx88LPK3tJisPhIGkPVC7tj5eX5X+SRYoFS3+TUlNT2bhxI+PGjXPX2e12unbtyvr167PcZ/369YwZMyZDXY8ePVi0aFGW26ekpJCS8u9S4HFxcYD5B8XhcFzhM/jXgXULqLzqAXxtDo4ZpXkwZRS/ra4Dq3/Is3PIhTz5318brA6iBLBD7Kl8PYPNBl4edrzsNrw87Hh62PA8/7OXhw1Pux0vz/P/e5zf5vzjPp52fL3seHt6uH/28bTj4+mBz/mffc8/5uNpx8fr35/NbT3w9rTj7+2Bn5f5c94wSEu79JVK6dL/DuXl3yPJmtq6YORXO+fkeJYmNydPnsTpdBIREZGhPiIigu3bt2e5T3R0dJbbR0dHZ7n9lClTmDRpUqb65cuX4+/vn8vIMzt9OpGyePOLqx6Ppd3HGVsQ3rZLf0XN9J3OdvHHLvf9LyfHysm+/y3m5bEut+/lvvTm5FiXi8N2wfls6Y/b/v35wsfAXMzswrLtgm3NsnGJxy613/njX3D+LOP7z2P/jclmM49hs5n1mX82zJ8vs136NraL/JxxeyPLfT1s4Gk3f/Y8X/awgYfd/D9PL9ZJO/8vCynn/xVWK1assDqEEkNtXTDyup0TEzPf6uNiin0f6Lhx4zL09MTFxVGpUiW6d+9OcHBwnp4rLaYFZ3/fzZruPfDy0oqd+cXhcLBixQq6deumds5HaueCoXYuOGrrgpFf7Zw+8pIdliY3YWFheHh4EBMTk6E+JiaGyMis14GJjIzM0fY+Pj74+GRejMrLyyvv39wRdcC2N3+OLZmonQuG2rlgqJ0Ljtq6YOR1O+fkWHk1wJwr3t7eNGvWjJUrV7rrXC4XK1eupHXr1lnu07p16wzbg9n1dbHtRUREpGSxfFhqzJgxDBkyhObNm9OiRQtmzJhBQkKC++qpwYMHU6FCBaZMmQLA6NGj6dChA9OmTeO6667j888/5/fff2fWrFlWPg0REREpJCxPbgYNGsSJEyeYMGEC0dHRNGnShKVLl7onDR88eBC7/d8OpjZt2vDpp5/y9NNP8+STT1KzZk0WLVpUKNa4EREREetZntwAjBo1ilGjRmX52Jo1azLVDRw4kIEDB+ZzVCIiIlIUWTrnRkRERCSvKbkRERGRYkXJjYiIiBQrSm5ERESkWFFyIyIiIsWKkhsREREpVpTciIiISLGi5EZERESKFSU3IiIiUqwUihWKC5JhGEDObp2eXQ6Hg8TEROLi4nTH2Xykdi4YaueCoXYuOGrrgpFf7Zz+uZ3+OX4pJS65iY+PB6BSpUoWRyIiIiI5FR8fT0hIyCW3sRnZSYGKEZfLxdGjRwkKCsJms+XpsePi4qhUqRKHDh0iODg4T48t/1I7Fwy1c8FQOxcctXXByK92NgyD+Ph4ypcvn+GG2lkpcT03drudihUr5us5goOD9YtTANTOBUPtXDDUzgVHbV0w8qOdL9djk04TikVERKRYUXIjIiIixYqSmzzk4+PDxIkT8fHxsTqUYk3tXDDUzgVD7Vxw1NYFozC0c4mbUCwiIiLFm3puREREpFhRciMiIiLFipIbERERKVaU3IiIiEixouQmh958802ioqLw9fWlZcuWbNiw4ZLbz58/nzp16uDr60vDhg1ZvHhxAUVatOWknd99913atWtHqVKlKFWqFF27dr3s6yKmnL6f033++efYbDb69u2bvwEWEzlt59jYWEaOHEm5cuXw8fGhVq1a+tuRDTlt5xkzZlC7dm38/PyoVKkSDz/8MMnJyQUUbdH0448/0rt3b8qXL4/NZmPRokWX3WfNmjVcddVV+Pj4UKNGDebOnZvvcWJItn3++eeGt7e3MXv2bOPvv/827r77biM0NNSIiYnJcvuff/7Z8PDwMKZOnWr8888/xtNPP214eXkZW7duLeDIi5actvOtt95qvPnmm8Yff/xhbNu2zRg6dKgREhJiHD58uIAjL1py2s7p9u3bZ1SoUMFo166dccMNNxRMsEVYTts5JSXFaN68udGrVy9j7dq1xr59+4w1a9YYmzdvLuDIi5actvMnn3xi+Pj4GJ988omxb98+Y9myZUa5cuWMhx9+uIAjL1oWL15sPPXUU8bChQsNwPjqq68uuf3evXsNf39/Y8yYMcY///xjvP7664aHh4exdOnSfI1TyU0OtGjRwhg5cqS77HQ6jfLlyxtTpkzJcvubbrrJuO666zLUtWzZ0hgxYkS+xlnU5bSd/ystLc0ICgoyPvjgg/wKsVjITTunpaUZbdq0Md577z1jyJAhSm6yIaft/PbbbxvVqlUzUlNTCyrEYiGn7Txy5Eijc+fOGerGjBljtG3bNl/jLE6yk9w89thjRv369TPUDRo0yOjRo0c+RmYYGpbKptTUVDZu3EjXrl3ddXa7na5du7J+/fos91m/fn2G7QF69Ohx0e0ld+38X4mJiTgcDkqXLp1fYRZ5uW3nZ599lrJly3LXXXcVRJhFXm7a+ZtvvqF169aMHDmSiIgIGjRowAsvvIDT6SyosIuc3LRzmzZt2Lhxo3voau/evSxevJhevXoVSMwlhVWfgyXuxpm5dfLkSZxOJxERERnqIyIi2L59e5b7REdHZ7l9dHR0vsVZ1OWmnf/r8ccfp3z58pl+oeRfuWnntWvX8v7777N58+YCiLB4yE077927l1WrVnHbbbexePFidu/ezf3334/D4WDixIkFEXaRk5t2vvXWWzl58iTXXHMNhmGQlpbGvffey5NPPlkQIZcYF/scjIuLIykpCT8/v3w5r3pupFh58cUX+fzzz/nqq6/w9fW1OpxiIz4+njvuuIN3332XsLAwq8Mp1lwuF2XLlmXWrFk0a9aMQYMG8dRTTzFz5kyrQytW1qxZwwsvvMBbb73Fpk2bWLhwId999x2TJ0+2OjTJA+q5yaawsDA8PDyIiYnJUB8TE0NkZGSW+0RGRuZoe8ldO6d75ZVXePHFF/n+++9p1KhRfoZZ5OW0nffs2cP+/fvp3bu3u87lcgHg6enJjh07qF69ev4GXQTl5v1crlw5vLy88PDwcNfVrVuX6OhoUlNT8fb2zteYi6LctPP48eO54447GD58OAANGzYkISGBe+65h6eeegq7Xd/988LFPgeDg4PzrdcG1HOTbd7e3jRr1oyVK1e661wuFytXrqR169ZZ7tO6desM2wOsWLHiottL7toZYOrUqUyePJmlS5fSvHnzggi1SMtpO9epU4etW7eyefNm978+ffrQqVMnNm/eTKVKlQoy/CIjN+/ntm3bsnv3bnfyCLBz507KlSunxOYictPOiYmJmRKY9ITS0C0X84xln4P5Ol25mPn8888NHx8fY+7cucY///xj3HPPPUZoaKgRHR1tGIZh3HHHHcYTTzzh3v7nn382PD09jVdeecXYtm2bMXHiRF0Kng05becXX3zR8Pb2NhYsWGAcO3bM/S8+Pt6qp1Ak5LSd/0tXS2VPTtv54MGDRlBQkDFq1Chjx44dxrfffmuULVvWeO6556x6CkVCTtt54sSJRlBQkPHZZ58Ze/fuNZYvX25Ur17duOmmm6x6CkVCfHy88ccffxh//PGHARjTp083/vjjD+PAgQOGYRjGE088Ydxxxx3u7dMvBX/00UeNbdu2GW+++aYuBS+MXn/9daNy5cqGt7e30aJFC+OXX35xP9ahQwdjyJAhGbb/4osvjFq1ahne3t5G/fr1je+++66AIy6actLOVapUMYBM/yZOnFjwgRcxOX0/X0jJTfbltJ3XrVtntGzZ0vDx8TGqVatmPP/880ZaWloBR1305KSdHQ6H8cwzzxjVq1c3fH19jUqVKhn333+/cebMmYIPvAhZvXp1ln9v09t2yJAhRocOHTLt06RJE8Pb29uoVq2aMWfOnHyP02YY6n8TERGR4kNzbkRERKRYUXIjIiIixYqSGxERESlWlNyIiIhIsaLkRkRERIoVJTciIiJSrCi5ERERkWJFyY2I5KmhQ4fSt29fq8MQkRJMyY2IiIgUK0puRKTQMwyDtLQ0q8PIltTUVKtDECnxlNyISI4tWLCAhg0b4ufnR5kyZejatSsJCQkZtnnllVcoV64cZcqUYeTIkTgcDvdjH330Ec2bNycoKIjIyEhuvfVWjh8/7n58zZo12Gw2lixZQrNmzfDx8WHt2rW4XC6mTJlC1apV8fPzo3HjxixYsOCSsdpsNhYtWpShLjQ0lLlz5wJmMjJq1CjKlSuHr68vVapUYcqUKe5tY2NjGT58OOHh4QQHB9O5c2e2bNnifvyZZ56hSZMmvPfee1StWhVfX99st5GI5A9PqwMQkaLl2LFj3HLLLUydOpV+/foRHx/PTz/9xIW3qVu9ejXlypVj9erV7N69m0GDBtGkSRPuvvtuABwOB5MnT6Z27docP36cMWPGMHToUBYvXpzhXE888QSvvPIK1apVo1SpUkyZMoWPP/6YmTNnUrNmTX788Uduv/12wsPD6dChQ66ez2uvvcY333zDF198QeXKlTl06BCHDh1yPz5w4ED8/PxYsmQJISEhvPPOO3Tp0oWdO3dSunRpAHbv3s2XX37JwoUL8fDwyFYbiUg+yvdbc4pIsbJx40YDMPbv35/l40OGDDGqVKmS4S7WAwcONAYNGnTRY/72228GYMTHxxuG8e+dhxctWuTeJjk52fD39zfWrVuXYd+77rrLuOWWWy56bMD46quvMtSFhIS470z8wAMPGJ07dzZcLlemfX/66ScjODjYSE5OzlBfvXp145133jEMwzAmTpxoeHl5GcePH3c/frk2EpH8pWEpEcmRxo0b06VLFxo2bMjAgQN59913OXPmTIZt6tevj4eHh7tcrly5DMNOGzdupHfv3lSuXJmgoCB3r8vBgwczHKd58+bun3fv3k1iYiLdunUjMDDQ/e/DDz9kz549uX4+Q4cOZfPmzdSuXZsHH3yQ5cuXux/bsmUL586do0yZMhnOuW/fvgznrFKlCuHh4TlqIxHJPxqWEpEc8fDwYMWKFaxbt47ly5fz+uuv89RTT/Hrr79StWpVALy8vDLsY7PZcLlcACQkJNCjRw969OjBJ598Qnh4OAcPHqRHjx6ZJuMGBAS4fz537hwA3333HRUqVMiwnY+Pz0XjtdlsmYaDLpz/c9VVV7Fv3z6WLFnC999/z0033UTXrl1ZsGAB586do1y5cqxZsybTcUNDQ7OMM7ttJCL5R8mNiOSYzWajbdu2tG3blgkTJlClShW++uorxowZc9l9t2/fzqlTp3jxxRepVKkSAL///vtl96tXrx4+Pj4cPHgwR/NrwsPDOXbsmLu8a9cuEhMTM2wTHBzMoEGDGDRoEAMGDODaa6/l9OnTXHXVVURHR+Pp6UlUVFS2zwlX1kYicmWU3IhIjvz666+sXLmS7t27U7ZsWX799VdOnDhB3bp1s7V/5cqV8fb25vXXX+fee+/lr7/+YvLkyZfdLygoiLFjx/Lwww/jcrm45pprOHv2LD///DPBwcEMGTIky/06d+7MG2+8QevWrXE6nTz++OMZepamT59OuXLlaNq0KXa7nfnz5xMZGUloaChdu3aldevW9O3bl6lTp1KrVi2OHj3Kd999R79+/TIMm+VlG4nIlVFyIyI5EhwczI8//siMGTOIi4ujSpUqTJs2jZ49e2Zr//DwcObOncuTTz7Ja6+9xlVXXcUrr7xCnz59Lrvv5MmTCQ8PZ8qUKezdu5fQ0FCuuuoqnnzyyYvuM23aNIYNG0a7du0oX748r776Khs3bnQ/HhQUxNSpU9m1axceHh5cffXVLF68GLvdnJK4ePFinnrqKYYNG8aJEyeIjIykffv2RERE5FsbiciVsRn/HYwWERERKcJ0tZSIiIgUK0puREREpFhRciMiIiLFipIbERERKVaU3IiIiEixouRGREREihUlNyIiIlKsKLkRERGRYkXJjYiIiBQrSm5ERESkWFFyIyIiIsWKkhsREREpVv4fwhQXp4W1HeUAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt \n", "\n", "gini_coefficient = gini(\n", " df_users[\"num_changes\"].values # we need an array\n", ")\n", "print(f\"Gini coefficient: {gini_coefficient}\")\n", "\n", "# calculate lorenz curve\n", "lorenz_curve = lorenz(\n", " df_users[\"num_changes\"].values # we need an array\n", ")\n", "\n", "# we need the X values to be between 0.0 to 1.0\n", "plt.plot(np.linspace(0.0, 1.0, lorenz_curve.size), lorenz_curve)\n", "plt.plot([0,1], [0,1]) # plot the straight line perfect equality curve\n", "\n", "plt.title(\"contribution inequality based on changesets\")\n", "plt.xlabel(\"share users\")\n", "plt.ylabel(\"share changes\")\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "f55546ef", "metadata": {}, "source": [ "#### Filter changesets for different user groups\n", "Here we filter changesets by comments and users.\n", "Changeset comments offer another quick way to differentiate different types of mapping. Here we want to check on organized mapping activities.\n", "\n", "Filter for all changesets which contain \"hotosm\" in the changeset comment.\n", "Save the filtered data into the geopackage." ] }, { "cell_type": "code", "execution_count": 32, "id": "03622c84", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "'hot users: 899'" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# define keyword that we'll use to filter\n", "keyword = \"hotosm\"\n", "\n", "# filter by user_names defined in csv file\n", "df_filtered_hotosm = df[\n", " df[\"comment\"].str.contains(\n", " keyword,\n", " case=False, # case insensitive\n", " na=False # ignore changesets without comments\n", " )\n", "]\n", "\n", "plt.scatter(\n", " df_filtered_hotosm[\"geometry\"].x, # get x coordinate from geometry\n", " df_filtered_hotosm[\"geometry\"].y, # get y coordinate from geometry\n", " alpha=0.01 # make sure to use a very low value here to avoid too much overplotting\n", ")\n", "plt.title(\"hotosm\")\n", "plt.show()\n", "\n", "# write filtered dataframe to geopackage\n", "df_filtered_hotosm.to_file(\n", " \"changesets.gpkg\",\n", " layer=\"changesets_hotosm_centroids\",\n", " driver=\"GPKG\"\n", ")\n", "\n", "display(f\"hot users: {df_filtered_hotosm['user_id'].nunique()}\")" ] }, { "cell_type": "markdown", "id": "5a283c57", "metadata": {}, "source": [ "When companies map in OSM they often make public which users are mapping on their behalf. Filtering for users in changesets is another way to investigate the impact of corporate mapping on overall OSM mapping. Both companies document their organized editing activity. For Facebook you can check the OSM Wiki and for Apple you will find information on their OSM mappers on Github.\n", "\n", "Filter for all changesets created by users listed in a) facebook_users.csv and b) apple_users.csv.\n", "Save the filtered data into the geopackage." ] }, { "cell_type": "code", "execution_count": 31, "id": "eb0348e1", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "'apple_users.csv: 433'" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "\n", "files = [\n", " \"facebook_users.csv\",\n", " \"apple_users.csv\"\n", "]\n", "\n", "for file in files:\n", " corporate_mappers_df = pd.read_csv(file)\n", "\n", " # filter by user_names defined in csv file\n", " df_filtered = df[\n", " df[\"user_name\"].isin(corporate_mappers_df[\"user_name\"])\n", " ]\n", " \n", " plt.scatter(\n", " df_filtered[\"geometry\"].x, # get x coordinate from geometry\n", " df_filtered[\"geometry\"].y, # get y coordinate from geometry\n", " alpha=0.01 # make sure to use a very low value here to avoid too much overplotting\n", " )\n", " plt.title(file)\n", " plt.show()\n", "\n", " #write filtered dataframe to geopackage\n", " df_filtered.to_file(\n", " \"changesets.gpkg\",\n", " layer=\"changesets_facebook_centroids\",\n", " driver=\"GPKG\"\n", " )\n", "\n", " display(f\"{file}: {df_filtered['user_id'].nunique()}\")" ] }, { "cell_type": "markdown", "id": "1562aa08", "metadata": {}, "source": [ "### 4. Conclusion\n", "\n", "Obviously our analysis has been relatively simple here, since we did not look into what has actually been mapped. Nevertheless, looking at the metadata captured in changesets is often a good starting point to test assumptions. Often you will do a more detailed analysis using the actual OSM data (e.g. contributions) in a subsequent step when you know better what to look out for." ] } ], "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": "3.10.6" } }, "nbformat": 4, "nbformat_minor": 5 }