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829 | 829 | },
|
830 | 830 | {
|
831 | 831 | "cell_type": "code",
|
832 |
| - "execution_count": 28, |
| 832 | + "execution_count": 37, |
833 | 833 | "metadata": {},
|
834 | 834 | "outputs": [
|
835 | 835 | {
|
836 | 836 | "data": {
|
837 | 837 | "application/vnd.jupyter.widget-view+json": {
|
838 |
| - "model_id": "e6513e852325493fbba43fc3fe118cbc", |
| 838 | + "model_id": "2e18ea7ed53b4d9bafcab7a9beb4574f", |
839 | 839 | "version_major": 2,
|
840 | 840 | "version_minor": 1
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841 | 841 | },
|
842 | 842 | "text/plain": [
|
843 | 843 | "Map(center=[4741230.57328505, -8594330.124312427], extent={'xmin': -8623829.789372643, 'ymin': 4703269.4188818…"
|
844 | 844 | ]
|
845 | 845 | },
|
846 |
| - "execution_count": 28, |
| 846 | + "execution_count": 37, |
847 | 847 | "metadata": {},
|
848 | 848 | "output_type": "execute_result"
|
849 | 849 | }
|
|
855 | 855 | },
|
856 | 856 | {
|
857 | 857 | "cell_type": "code",
|
858 |
| - "execution_count": 30, |
| 858 | + "execution_count": 38, |
859 | 859 | "metadata": {},
|
860 |
| - "outputs": [ |
861 |
| - { |
862 |
| - "data": { |
863 |
| - "text/plain": [ |
864 |
| - "True" |
865 |
| - ] |
866 |
| - }, |
867 |
| - "execution_count": 30, |
868 |
| - "metadata": {}, |
869 |
| - "output_type": "execute_result" |
870 |
| - } |
871 |
| - ], |
| 860 | + "outputs": [], |
| 861 | + "source": [ |
| 862 | + "map_enriched.content.add(zip_enriched_layer) # visualizing boundaries" |
| 863 | + ] |
| 864 | + }, |
| 865 | + { |
| 866 | + "cell_type": "code", |
| 867 | + "execution_count": 39, |
| 868 | + "metadata": {}, |
| 869 | + "outputs": [], |
| 870 | + "source": [ |
| 871 | + "map_enriched.content.add(zip_enriched_layer) # visualizing population" |
| 872 | + ] |
| 873 | + }, |
| 874 | + { |
| 875 | + "cell_type": "code", |
| 876 | + "execution_count": 42, |
| 877 | + "metadata": {}, |
| 878 | + "outputs": [], |
872 | 879 | "source": [
|
873 |
| - "zip_enriched_sdf.spatial.plot(\n", |
874 |
| - " map_widget=map_enriched, renderer_type=\"u\", col=\"tsegname\", palette=\"pink\"\n", |
| 880 | + "zip_enriched_layer_sm = map_enriched.content.renderer(1).smart_mapping()\n", |
| 881 | + "zip_enriched_layer_sm.class_breaks_renderer(\n", |
| 882 | + " break_type=\"size\",\n", |
| 883 | + " field=\"population\"\n", |
875 | 884 | ")"
|
876 | 885 | ]
|
877 | 886 | },
|
|
902 | 911 | "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."
|
903 | 912 | ]
|
904 | 913 | },
|
905 |
| - { |
906 |
| - "cell_type": "code", |
907 |
| - "execution_count": 31, |
908 |
| - "metadata": {}, |
909 |
| - "outputs": [], |
910 |
| - "source": [ |
911 |
| - "map_enriched.content.add(zip_enriched_layer)" |
912 |
| - ] |
913 |
| - }, |
914 |
| - { |
915 |
| - "cell_type": "code", |
916 |
| - "execution_count": 32, |
917 |
| - "metadata": {}, |
918 |
| - "outputs": [], |
919 |
| - "source": [ |
920 |
| - "zip_enriched_layer_sm = map_enriched.content.renderer(0).smart_mapping()\n", |
921 |
| - "zip_enriched_layer_sm.class_breaks_renderer(\n", |
922 |
| - " break_type=\"size\",\n", |
923 |
| - " field=\"point_count\"\n", |
924 |
| - ")" |
925 |
| - ] |
926 |
| - }, |
927 | 914 | {
|
928 | 915 | "cell_type": "markdown",
|
929 | 916 | "metadata": {},
|
|
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