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drop unsupported rendering logic
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samples/04_gis_analysts_data_scientists/analyze_patterns_in_construction_permits_part2.ipynb

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@@ -829,21 +829,21 @@
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 37,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "e6513e852325493fbba43fc3fe118cbc",
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"model_id": "2e18ea7ed53b4d9bafcab7a9beb4574f",
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"version_major": 2,
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"version_minor": 1
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},
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"text/plain": [
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"Map(center=[4741230.57328505, -8594330.124312427], extent={'xmin': -8623829.789372643, 'ymin': 4703269.4188818…"
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]
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},
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"execution_count": 28,
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"execution_count": 37,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -855,23 +855,32 @@
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 38,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 30,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"map_enriched.content.add(zip_enriched_layer) # visualizing boundaries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {},
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"outputs": [],
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"source": [
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"map_enriched.content.add(zip_enriched_layer) # visualizing population"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"metadata": {},
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"outputs": [],
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"source": [
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"zip_enriched_sdf.spatial.plot(\n",
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" map_widget=map_enriched, renderer_type=\"u\", col=\"tsegname\", palette=\"pink\"\n",
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"zip_enriched_layer_sm = map_enriched.content.renderer(1).smart_mapping()\n",
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"zip_enriched_layer_sm.class_breaks_renderer(\n",
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" break_type=\"size\",\n",
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" field=\"population\"\n",
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")"
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]
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},
@@ -902,28 +911,6 @@
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"Although Tapestry segments are based on several demographic characteristics, you could also perform this analysis with other variables. For instance, you could determine if there is a correlation between high permit activity and high population growth. Is a young population or a high income level a stronger indicator of growth? You can answer these questions and others with the analysis tools at your disposal. For the purposes of this lesson, however, your results are satisfactory."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [],
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"source": [
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"map_enriched.content.add(zip_enriched_layer)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {},
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"outputs": [],
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"source": [
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"zip_enriched_layer_sm = map_enriched.content.renderer(0).smart_mapping()\n",
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"zip_enriched_layer_sm.class_breaks_renderer(\n",
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" break_type=\"size\",\n",
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" field=\"point_count\"\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},

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