diff --git a/docs/_toc.yml b/docs/_toc.yml index 554ac4e..3ecfe7d 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -10,7 +10,10 @@ parts: - file: docs/foundational_datasets_and_data_products.md - caption: Data Products chapters: - - file: notebooks/nighttime-lights/nighttime-lights.ipynb + - file: notebooks/nighttime-lights/README + sections: + - file: notebooks/nighttime-lights/nighttime-lights.ipynb + - file: notebooks/nighttime-lights/nighttime-lights-in-jordan.ipynb - file: notebooks/conflict/acled.ipynb - caption: Acknowledgements chapters: diff --git a/notebooks/nighttime-lights/README.md b/notebooks/nighttime-lights/README.md new file mode 100644 index 0000000..3aa5b24 --- /dev/null +++ b/notebooks/nighttime-lights/README.md @@ -0,0 +1,46 @@ +# Nighttime Lights Trends + +Analyzing conflict dynamics through the lens of [NASA's Black Marble Nighttime Lights](https://blackmarble.gsfc.nasa.gov) dataset opens a unique window into the often-hidden facets of global unrest. In a world marked by diverse forms of conflict, from armed confrontations to civil unrest, the dataset offers an unconventional yet powerful tool for understanding the ripple effects of these conflicts on human settlements and infrastructure. By tracking nighttime light variations and disruptions, we can unearth vital insights into population displacement, economic destabilization, and the societal impacts of conflict. This analysis explores the potential of the Black Marble Nighttime Lights dataset to not only detect areas affected by conflict but also to quantify the extent of its influence on human lives and livelihoods, providing a valuable perspective on the multifaceted consequences of conflict worldwide. + +(nighttime-lights-limitations)= +## Limitations + +Using nighttime lights to estimate macroeconomic indicators during conflict may be a valuable approach, but it comes with several assumptions and limitations. Here's a list of some of the key assumptions and limitations: + +```{caution} +**Assumptions:** + +- **Luminosity Reflects Economic Activity:** The approach assumes that the level of nighttime lights is a reliable proxy for economic activity. It presupposes that areas with brighter lights correspond to higher economic productivity. + +- **Baseline Data Availability:** It assumes the availability of baseline nighttime lights data before the onset of the conflict. The accuracy of the estimates depends on the quality and relevance of this baseline data. + +- **Spatial Distribution:** The method assumes that nighttime lights are evenly distributed within a given geographic area and that changes in luminosity accurately reflect changes in economic activity across all locations. + + +**Limitations:** + +- **Confounding Factors and Data Interpretation:** The approach may require subjective interpretation, as it may not distinguish between reduced lighting due to conflict and reduced lighting due to other factors. Changes in nighttime lights can be influenced by factors other than economic activity, such as energy conservation measures, urban development, or seasonal variations. + +- **Generalization:** The approach might lead to overgeneralization, as a reduction in nighttime lights can be associated with various economic outcomes, from minor disruptions to severe economic downturns. + +- **Alternative Explanations:** Changes in nighttime lights can result from factors other than conflict, such as urban development, changes in economic activities, or natural disasters. Therefore, it may not always be clear whether a decline in nighttime lights is solely due to conflict. + +- **Geopolitical Factors:** The dataset may be subject to geopolitical biases, with some areas having less comprehensive coverage due to political reasons. + +- **Data Lag:** There can be a significant time lag between the occurrence of a conflict event and its reflection in the nighttime lights dataset. This lag may limit the dataset's utility for real-time conflict monitoring. + +- **Resolution and Urban Bias:** The dataset's spatial resolution may not be fine enough to capture small villages or isolated conflict events. It may also have an urban bias, making it less suitable for analyzing rural or remote conflicts. +``` + +To address these assumptions and limitations, it is crucial to complement nighttime lights data analysis with other sources of information and adopt a cautious and context-aware approach when interpreting the findings. + +## References + +{cite:empty}`ROMAN2018113` + +```{bibliography} +:filter: docname in docnames +:style: plain +``` + + diff --git a/notebooks/nighttime-lights/nighttime-lights-in-jordan.ipynb b/notebooks/nighttime-lights/nighttime-lights-in-jordan.ipynb new file mode 100644 index 0000000..d83fc86 --- /dev/null +++ b/notebooks/nighttime-lights/nighttime-lights-in-jordan.ipynb @@ -0,0 +1,1682 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "73b42bfe-7179-4172-b641-dfe5da287d55", + "metadata": {}, + "source": [ + "# Nighttime Lights Trends in Jordan\n", + "\n", + "The purpose of this notebook is to conduct an examination of the spatial and temporal distribution of artificial lights during the night across various regions in Jordan. The dataset utilized is derived from [NASA's Black Marble](https://blackmarble.gsfc.nasa.gov) and Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ba22d742-7497-46df-ba8e-e0988f023ca3", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "import os\n", + "from datetime import datetime\n", + "\n", + "import colorcet as cc\n", + "import dask.dataframe as dd\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from blackmarble.bm_extract import bm_extract\n", + "from blackmarble.bm_raster import bm_raster\n", + "from bokeh.models import ColumnDataSource, HoverTool, Legend, Text, Title\n", + "from bokeh.plotting import figure, output_notebook, show\n", + "from gadm import GADMDownloader\n", + "from matplotlib.font_manager import get_font_names\n", + "\n", + "sns.set_theme()\n", + "sns.set_style(\"whitegrid\")\n", + "\n", + "mpl.rcParams[\"font.family\"] = \"Arial\"\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "id": "cc878977-7626-4f07-b2a7-d8d727e05134", + "metadata": {}, + "source": [ + "## Data\n" + ] + }, + { + "cell_type": "markdown", + "id": "f1cce32c-f001-4324-9aeb-1246a3044457", + "metadata": {}, + "source": [ + "#### Define Region of Interest\n", + "\n", + "Define region of interest for where we want to download nighttime lights data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5f3f2e01-0886-4ffb-8325-c1d003b071fd", + "metadata": { + "editable": true, + "scrolled": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "COUNTRY = GADMDownloader(version=\"4.0\").get_shape_data_by_country_name(\n", + " country_name=\"Jordan\", ad_level=2\n", + ")\n", + "COUNTRY.explore()" + ] + }, + { + "cell_type": "markdown", + "id": "d1cc7df3-6ef4-4a11-88ea-c518ba600d41", + "metadata": {}, + "source": [ + "### Black Marble \n", + "\n", + "[NASA's Black Marble](https://blackmarble.gsfc.nasa.gov) VIIRS (Visible Infrared Imaging Radiometer Suite) Nighttime Lights dataset represents a remarkable advancement in our ability to monitor and understand nocturnal light emissions on a global scale. By utilizing cutting-edge satellite technology and image processing techniques, the Black Marble VIIRS dataset offers a comprehensive and high-resolution view of the Earth's nighttime illumination patterns." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "42d1f400-2063-4d39-8c3b-4ed4052281f4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "dates = (\n", + " pd.date_range(\"2023-11-01\", \"2023-11-14\", freq=\"D\").strftime(\"%Y-%m-%d\").tolist()\n", + ")\n", + "\n", + "df = bm_extract(\n", + " roi_sf=COUNTRY,\n", + " product_id=\"VNP46A2\",\n", + " date=dates,\n", + " bearer=os.environ.get(\"BLACKMARBLE_TOKEN\"),\n", + " output_location_type=\"file\",\n", + " file_dir=\"data\",\n", + " file_prefix=\"jor_\",\n", + " aggregation_fun=[\"count\", \"mean\", \"min\", \"max\", \"median\", \"sum\"],\n", + " quiet=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "058fc345-a364-4719-94a9-30f225d469d7", + "metadata": {}, + "source": [ + "Now that the data was acquired, let's read the data files," + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "883bfe76-d1c0-423f-a1fb-6579b02a182f", + "metadata": { + "editable": true, + "scrolled": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ID_0COUNTRYNAME_1NL_NAME_1ID_2NAME_2VARNAME_2NL_NAME_2TYPE_2ENGTYPE_2CC_2HASC_2ntl_minntl_maxntl_meanntl_countntl_sumntl_mediandate
0JORJordanAjlunJOR.1.1_1AjlounAjlunNahiaSub-ProvinceJO.AJ.AJ26.028.027.5000004110.028.02022-01-01
1JORJordanAjlunJOR.1.2_1KofranjahNahiaSub-ProvinceJO.AJ.KFNaNNaNNaN0NaNNaN2022-01-01
2JORJordanAmmanJOR.2.1_1AmmanNahiaSub-ProvinceJO.AM.AM21.01610.0305.159483928283188.0316.02022-01-01
3JORJordanAmmanJOR.2.2_1JizehAl-JizaNahiaSub-ProvinceJO.AM.JI0.01072.07.87366120532161662.02.02022-01-01
4JORJordanAmmanJOR.2.3_1MowaqqarAl-MwwqqarNahiaSub-ProvinceJO.AM.MO3.019.012.38775549607.012.02022-01-01
............................................................
47JORJordanTafilahJOR.11.2_1HesaAl-HasaNahiaSub-ProvinceJO.AT.HE1.0717.014.148782455764476.04.02023-11-05
48JORJordanTafilahJOR.11.3_1TafilehAl-TafilaNahiaSub-ProvinceJO.AT.AT1.01061.028.0652036104171310.05.02023-11-05
49JORJordanZarqaJOR.12.1_1AzraqAl-AzraqNahiaSub-ProvinceJO.AZ.ZQ0.01457.011.39288011179127361.09.02023-11-05
50JORJordanZarqaJOR.12.2_1BierainBirinNahiaSub-ProvinceJO.AZ.BR20.01115.0155.747934726113073.095.02023-11-05
51JORJordanZarqaJOR.12.3_1ZarqaAz-ZarqaNahiaSub-ProvinceJO.AZ.AZ4.02844.0197.9823623175628594.079.02023-11-05
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" + ], + "text/plain": [ + " ID_0 COUNTRY NAME_1 NL_NAME_1 ID_2 NAME_2 VARNAME_2 \\\n", + "0 JOR Jordan Ajlun JOR.1.1_1 Ajloun Ajlun \n", + "1 JOR Jordan Ajlun JOR.1.2_1 Kofranjah \n", + "2 JOR Jordan Amman JOR.2.1_1 Amman \n", + "3 JOR Jordan Amman JOR.2.2_1 Jizeh Al-Jiza \n", + "4 JOR Jordan Amman JOR.2.3_1 Mowaqqar Al-Mwwqqar \n", + ".. ... ... ... ... ... ... ... \n", + "47 JOR Jordan Tafilah JOR.11.2_1 Hesa Al-Hasa \n", + "48 JOR Jordan Tafilah JOR.11.3_1 Tafileh Al-Tafila \n", + "49 JOR Jordan Zarqa JOR.12.1_1 Azraq Al-Azraq \n", + "50 JOR Jordan Zarqa JOR.12.2_1 Bierain Birin \n", + "51 JOR Jordan Zarqa JOR.12.3_1 Zarqa Az-Zarqa \n", + "\n", + " NL_NAME_2 TYPE_2 ENGTYPE_2 CC_2 HASC_2 ntl_min ntl_max \\\n", + "0 Nahia Sub-Province JO.AJ.AJ 26.0 28.0 \n", + "1 Nahia Sub-Province JO.AJ.KF NaN NaN \n", + "2 Nahia Sub-Province JO.AM.AM 21.0 1610.0 \n", + "3 Nahia Sub-Province JO.AM.JI 0.0 1072.0 \n", + "4 Nahia Sub-Province JO.AM.MO 3.0 19.0 \n", + ".. ... ... ... ... ... ... ... \n", + "47 Nahia Sub-Province JO.AT.HE 1.0 717.0 \n", + "48 Nahia Sub-Province JO.AT.AT 1.0 1061.0 \n", + "49 Nahia Sub-Province JO.AZ.ZQ 0.0 1457.0 \n", + "50 Nahia Sub-Province JO.AZ.BR 20.0 1115.0 \n", + "51 Nahia Sub-Province JO.AZ.AZ 4.0 2844.0 \n", + "\n", + " ntl_mean ntl_count ntl_sum ntl_median date \n", + "0 27.500000 4 110.0 28.0 2022-01-01 \n", + "1 NaN 0 NaN NaN 2022-01-01 \n", + "2 305.159483 928 283188.0 316.0 2022-01-01 \n", + "3 7.873661 20532 161662.0 2.0 2022-01-01 \n", + "4 12.387755 49 607.0 12.0 2022-01-01 \n", + ".. ... ... ... ... ... \n", + "47 14.148782 4557 64476.0 4.0 2023-11-05 \n", + "48 28.065203 6104 171310.0 5.0 2023-11-05 \n", + "49 11.392880 11179 127361.0 9.0 2023-11-05 \n", + "50 155.747934 726 113073.0 95.0 2023-11-05 \n", + "51 197.982362 3175 628594.0 79.0 2023-11-05 \n", + "\n", + "[33956 rows x 19 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = dd.read_csv(\n", + " \"data/jor_VNP46A2*.csv\",\n", + " parse_dates=[\"date\"],\n", + ").compute()\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "0f210e44-11c6-4b85-8f54-b12637d135c9", + "metadata": {}, + "source": [ + "```{important}\n", + "The [VNP46A2 Daily Moonlight-adjusted Nighttime Lights (NTL) Product](https://blackmarble.gsfc.nasa.gov/VNP46A2.html) is available daily. However, due data quality, cloud cover or other factors, the data may not be available always at a specific location.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "a9c10d77-c7e3-45b4-8555-1c2812864bdf", + "metadata": {}, + "source": [ + "## Methodology\n", + "\n", + "Creating a time series of weekly radiance using NASA's Black Marble data involves several steps, including data acquisition, pre-processing, zonal statistics calculation, and time series generation. Below is a general methodology for this process." + ] + }, + { + "cell_type": "markdown", + "id": "c60b94ee-6f68-4896-91f5-c6be73966df3", + "metadata": {}, + "source": [ + "### Time Series Generation\n", + "\n", + "Organize the zonal statistics results in a tabular format, where each columnn corresponds to a specific zone, and rows represent the daily radiance values. Next, we aggregate the data on a weekly basis, computing the desired statistical metric (e.g., mean radiance) for each zone for each week. Finally, we will visualize the time series data to observe trends, patterns, and anomalies over time." + ] + }, + { + "cell_type": "markdown", + "id": "292ee023-083e-4dd9-8532-435f85405844", + "metadata": {}, + "source": [ + "### Aggregate by `mean`" + ] + }, + { + "cell_type": "markdown", + "id": "fba71980-61f5-43fc-90c3-17c72583e7e2", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "parameters" + ] + }, + "source": [ + "In this step, we compute the weekly aggregation of the zonal statistics by for each second-level administrative division and for each week. In this case, we use the `mean` and the start of the week." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "929ff32a-8b04-4a69-b81b-303240691064", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "parameters", + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# https://papermill.readthedocs.io/en/latest/usage-parameterize.html\n", + "VAR = \"ntl_mean\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8a0866cc-dc0c-419e-b5b7-1131d9a857fc", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ntl_mean
NAME_1AjlunAmmanAqabaBalqaIrbidJarashKarakMa`anMadabaMafraqTafilahZarqa
date
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2022-01-0275.069969202.4764058.505361134.089928106.84187671.38409727.26128615.19356925.74989846.62293521.71045281.786189
2022-01-0973.258399146.1980917.302361126.774726109.84838070.58138022.45253314.71577426.12981241.54718210.76175968.849111
2022-01-1666.562622206.67654710.440221103.589737101.89219561.86266028.89743615.90135022.49652546.51710420.46586082.027899
2022-01-2350.736298131.44964010.701375110.01011492.38372458.47305522.60563413.27654719.78780032.54410715.55813551.250123
.......................................
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97 rows × 12 columns

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" + ], + "text/plain": [ + " ntl_mean \\\n", + "NAME_1 Ajlun Amman Aqaba Balqa Irbid \n", + "date \n", + "2021-12-26 24.449405 100.560509 7.354693 69.469869 34.211491 \n", + "2022-01-02 75.069969 202.476405 8.505361 134.089928 106.841876 \n", + "2022-01-09 73.258399 146.198091 7.302361 126.774726 109.848380 \n", + "2022-01-16 66.562622 206.676547 10.440221 103.589737 101.892195 \n", + "2022-01-23 50.736298 131.449640 10.701375 110.010114 92.383724 \n", + "... ... ... ... ... ... \n", + "2023-10-01 82.834816 166.543513 10.767144 139.648121 135.710503 \n", + "2023-10-08 74.725720 268.089576 15.548408 118.383982 171.830205 \n", + "2023-10-15 77.643016 219.769259 15.844977 131.277761 182.848500 \n", + "2023-10-22 97.636910 246.901782 14.079673 149.398513 215.814159 \n", + "2023-10-29 86.800853 217.892463 14.177184 149.693422 193.366805 \n", + "\n", + " \\\n", + "NAME_1 Jarash Karak Ma`an Madaba Mafraq Tafilah \n", + "date \n", + "2021-12-26 26.072344 32.249152 14.246122 35.418149 21.112632 17.448854 \n", + "2022-01-02 71.384097 27.261286 15.193569 25.749898 46.622935 21.710452 \n", + "2022-01-09 70.581380 22.452533 14.715774 26.129812 41.547182 10.761759 \n", + "2022-01-16 61.862660 28.897436 15.901350 22.496525 46.517104 20.465860 \n", + "2022-01-23 58.473055 22.605634 13.276547 19.787800 32.544107 15.558135 \n", + "... ... ... ... ... ... ... \n", + "2023-10-01 72.164824 32.195193 14.636889 42.565155 46.562834 23.539344 \n", + "2023-10-08 79.998565 46.072971 22.191168 37.634376 59.277453 28.815367 \n", + "2023-10-15 88.222131 42.819114 23.521066 38.187475 60.871674 31.162284 \n", + "2023-10-22 93.072254 43.123620 24.703762 47.732346 55.785789 31.345070 \n", + "2023-10-29 93.819495 48.715216 22.026993 57.222549 67.613157 28.489876 \n", + "\n", + " \n", + "NAME_1 Zarqa \n", + "date \n", + "2021-12-26 27.433938 \n", + "2022-01-02 81.786189 \n", + "2022-01-09 68.849111 \n", + "2022-01-16 82.027899 \n", + "2022-01-23 51.250123 \n", + "... ... \n", + "2023-10-01 78.440840 \n", + "2023-10-08 79.310402 \n", + "2023-10-15 105.373608 \n", + "2023-10-22 107.245041 \n", + "2023-10-29 109.128964 \n", + "\n", + "[97 rows x 12 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "JO_2 = (\n", + " df.pivot_table(index=\"date\", columns=[\"NAME_2\"], values=[VAR]).resample(\"W\").mean()\n", + ")\n", + "\n", + "JO_1 = (\n", + " df.pivot_table(index=\"date\", columns=[\"NAME_1\"], values=[VAR], aggfunc=\"mean\")\n", + " .resample(\"W\", label=\"left\")\n", + " .mean()\n", + ")\n", + "JO_1" + ] + }, + { + "cell_type": "markdown", + "id": "f9183aba-d35b-4b8e-9f36-cf677d79ae2c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Now, we visualize, " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7f14a501-f0c0-4a98-8123-32e2dad20f94", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "data = JO_1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3a0390f3-2d52-4a37-9df6-cf05fdaa9517", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(\"bdfedb82-2ea8-4f78-b286-8a8d470d8e30\");\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; 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i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "if (force === true) {\n", + " display_loaded();\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(\"bdfedb82-2ea8-4f78-b286-8a8d470d8e30\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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Feedback: datalab@worldbank.org.\",\"text_font_size\":\"10pt\",\"text_font_style\":\"italic\"}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1029\",\"attributes\":{\"axis\":{\"id\":\"p1013\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1034\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1030\"}}}]}}]}};\n", + " const render_items = [{\"docid\":\"c5805c78-725b-4e4d-8ecb-5e01a86107f1\",\"roots\":{\"p1001\":\"b210b92c-79d3-4e8b-8dac-8acdf57a70af\"},\"root_ids\":[\"p1001\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p1001" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "p = figure(\n", + " title=\"Jordan: Weekly Nighttime Lights (2022-2023)\",\n", + " width=800,\n", + " height=700,\n", + " x_axis_label=\"Date\",\n", + " x_axis_type=\"datetime\",\n", + " y_axis_label=r\"Radiance [nW $$cm^{-2}$$ $$sr^{-1}$$]\",\n", + " tools=\"pan,wheel_zoom,box_zoom,reset,save,box_select\",\n", + ")\n", + "p.add_layout(\n", + " Title(\n", + " text=f\"Weekly Adm2 Radiance Average\",\n", + " text_font_size=\"12pt\",\n", + " text_font_style=\"italic\",\n", + " ),\n", + " \"above\",\n", + ")\n", + "p.add_layout(\n", + " Title(\n", + " text=f\"Data Source: NASA Black Marble. Creation date: {datetime.today().strftime('%d %B %Y')}. Feedback: datalab@worldbank.org.\",\n", + " text_font_size=\"10pt\",\n", + " text_font_style=\"italic\",\n", + " ),\n", + " \"below\",\n", + ")\n", + "p.add_layout(Legend(), \"right\")\n", + "\n", + "p.add_tools(\n", + " HoverTool(\n", + " tooltips=[\n", + " (\"Week\", \"@x{%F} (@x{%W of %Y})\"),\n", + " (\"Radiance\", \"@y{00.0}\"),\n", + " ],\n", + " formatters={\"@x\": \"datetime\"},\n", + " )\n", + ")\n", + "\n", + "for column, color in zip(data.columns, cc.b_glasbey_category10):\n", + " try:\n", + " r = p.line(\n", + " data.index,\n", + " data[column],\n", + " legend_label=column[1],\n", + " line_color=color,\n", + " line_width=2,\n", + " )\n", + " renderers.append(r)\n", + " except:\n", + " pass\n", + "\n", + "p.legend.location = \"bottom_left\"\n", + "p.legend.click_policy = \"hide\"\n", + "p.title.text_font_size = \"16pt\"\n", + "p.sizing_mode = \"scale_both\"\n", + "\n", + "output_notebook()\n", + "show(p)" + ] + }, + { + "cell_type": "markdown", + "id": "8868d736-1b5e-4c7e-ac6d-1911b07f3bba", + "metadata": {}, + "source": [ + "```{figure} ../../docs/images/logo.png\n", + "---\n", + "height: 0px\n", + "---\n", + "The indicator above consists of the weekly average of the daily zonal statistics (i.e., `mean`) derived from NASA Black Marble for each second-level administrative division. \n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "62013173-a8e8-475d-9ec8-0f3d5fe63b1e", + "metadata": {}, + "source": [ + "## Findings" + ] + }, + { + "cell_type": "markdown", + "id": "8846e1c2-4299-4778-9bf4-e522b0b6af1b", + "metadata": {}, + "source": [ + "### National Average Weekly Radiance" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "38eb48fc-6b58-4d5c-b5cd-ed29f41fe7d5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "JO_0 = JO_1.mean(axis=\"columns\").to_frame(VAR)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8de84d23-42b4-44bb-b478-d3ed311599d3", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input", + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "fig.suptitle(f\"Jordan: Weekly Nighttime Lights (2022-2023)\", y=0.95)\n", + "\n", + "JO_0.plot(ax=ax, marker=\"8\")\n", + "ax.set_title(\"Weekly National Radiance Average\")\n", + "ax.set_xlabel(\"Date\")\n", + "ax.set_ylabel(r\"Radiance [nW $cm^{-2}$ $sr^{-1}$]\")\n", + "ax.ticklabel_format(style=\"plain\", axis=\"y\")\n", + "\n", + "ax.annotate(\n", + " \"Data Source: NASA Black Marble VNP46A2 Gap Filled BRDF corrected DNB NTL\",\n", + " xy=(1.0, -0.2),\n", + " xycoords=\"axes fraction\",\n", + " ha=\"right\",\n", + " va=\"center\",\n", + " fontsize=10,\n", + ")\n", + "\n", + "sns.set_context(\"notebook\", font_scale=1.25, rc={\"lines.linewidth\": 2.5})\n", + "fig.autofmt_xdate()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e75cc298-16ec-48b8-a3b6-6a10864bd5b9", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "fig.tight_layout()\n", + "fig.savefig(\"jor_mean_weekly_radiance.png\", dpi=300)" + ] + }, + { + "cell_type": "markdown", + "id": "0a68040a-db5a-4e1f-b264-002a419e36b0", + "metadata": {}, + "source": [ + "## Limitations\n", + "\n", + "```{seealso}\n", + "{ref}`nighttime-lights-limitations`\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": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/nighttime-lights/nighttime-lights.ipynb b/notebooks/nighttime-lights/nighttime-lights.ipynb index 82bbded..0698b71 100644 --- a/notebooks/nighttime-lights/nighttime-lights.ipynb +++ b/notebooks/nighttime-lights/nighttime-lights.ipynb @@ -5,7 +5,7 @@ "id": "7776d467-20ad-4044-b10e-37b67bf0d914", "metadata": {}, "source": [ - "# Nighttime Lights Trends\n", + "## Nighttime Lights Trends in Gaza\n", "\n", "Analyzing conflict dynamics through the lens of [NASA's Black Marble Nighttime Lights](https://blackmarble.gsfc.nasa.gov) dataset opens a unique window into the often-hidden facets of global unrest. In a world marked by diverse forms of conflict, from armed confrontations to civil unrest, the dataset offers an unconventional yet powerful tool for understanding the ripple effects of these conflicts on human settlements and infrastructure. By tracking nighttime light variations and disruptions, we can unearth vital insights into population displacement, economic destabilization, and the societal impacts of conflict. This analysis explores the potential of the Black Marble Nighttime Lights dataset to not only detect areas affected by conflict but also to quantify the extent of its influence on human lives and livelihoods, providing a valuable perspective on the multifaceted consequences of conflict worldwide." ] @@ -13,7 +13,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "aca61659-9e96-4067-9523-12ebd252adfb", + "id": "5bcd9136-77df-4f63-ba42-bfa97a477e2a", "metadata": { "editable": true, "slideshow": { @@ -25,17 +25,28 @@ }, "outputs": [], "source": [ + "import math\n", + "import os\n", "from datetime import datetime\n", + "from math import pi\n", + "\n", + "import colorcet as cc\n", + "import dask.dataframe as dd\n", + "import folium\n", "import geopandas\n", + "import numpy as np\n", "import pandas as pd\n", - "\n", - "from bokeh.models import Div\n", - "from bokeh.layouts import gridplot, column as b_column\n", - "\n", + "from blackmarble.bm_extract import bm_extract\n", + "from blackmarble.bm_raster import bm_raster\n", + "from bokeh.layouts import column as b_column\n", + "from bokeh.layouts import gridplot\n", "from bokeh.models import (\n", + " BasicTicker,\n", " ColumnDataSource,\n", + " Div,\n", " HoverTool,\n", " Legend,\n", + " PrintfTickFormatter,\n", " Range1d,\n", " Span,\n", " TabPanel,\n", @@ -44,18 +55,7 @@ " Title,\n", ")\n", "from bokeh.plotting import figure, output_notebook, show\n", - "import math\n", - "from math import pi\n", - "\n", - "import pandas as pd\n", - "\n", - "from bokeh.models import BasicTicker, PrintfTickFormatter\n", - "from bokeh.plotting import figure, show\n", - "from bokeh.sampledata.unemployment1948 import data\n", - "from bokeh.transform import linear_cmap\n", - "import colorcet as cc\n", - "\n", - "import numpy as np" + "from bokeh.transform import linear_cmap" ] }, { @@ -73,8 +73,7 @@ }, "outputs": [], "source": [ - "group = lambda flat, size: [flat[i:i+size] for i in range(0,len(flat), size)]\n", - "COLORS = cc.b_glasbey_hv" + "group = lambda flat, size: [flat[i : i + size] for i in range(0, len(flat), size)]" ] }, { @@ -82,7 +81,224 @@ "id": "6edaf441-87a3-409a-bd07-6f99adb3a14f", "metadata": {}, "source": [ - "## Data\n", + "## Data" + ] + }, + { + "cell_type": "markdown", + "id": "23970210-dfe3-4dfb-a0ac-7ad651bc573d", + "metadata": {}, + "source": [ + "### Define Region of Interest\n", + "\n", + "Define region of interest for where NASA Black Marble will be downloaded." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "62f8e6b9-f8b2-483d-a3e0-9e239666c4d3", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PSE = geopandas.read_file(\"../../data/boundaries/gadm41_PSE_shp/gadm41_PSE_2.shp\")\n", + "PSE[PSE[\"NAME_1\"] == \"Gaza\"].explore()" + ] + }, + { + "cell_type": "markdown", + "id": "dc9751be-048d-4a41-ad17-6a23bbad7dc4", + "metadata": {}, + "source": [ "\n", "### Black Marble \n", "\n", @@ -91,7 +307,37 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, + "id": "29c59475-56b1-4e3a-9d55-6cdecc444118", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "dates = (\n", + " pd.date_range(\"2022-01-01\", \"2023-11-14\", freq=\"D\").strftime(\"%Y-%m-%d\").tolist()\n", + ")\n", + "\n", + "df = bm_extract(\n", + " roi_sf=ROI,\n", + " product_id=\"VNP46A2\",\n", + " date=dates,\n", + " bearer=os.environ.get(\"BLACKMARBLE_TOKEN\"),\n", + " output_location_type=\"file\",\n", + " file_dir=\"data\",\n", + " file_prefix=\"pse2_\",\n", + " aggregation_fun=[\"count\", \"mean\", \"min\", \"max\", \"median\", \"sum\"],\n", + " quiet=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "id": "e4979326-50b3-4572-a8d1-b043dd148e72", "metadata": { "editable": true, @@ -104,13 +350,231 @@ }, "outputs": [], "source": [ - "NTL = pd.read_csv(\"../../data/nighttimelights/ntl_bm_daily.csv\", parse_dates=[\"date\"], index_col=[\"date\"])\n", - "NTL = NTL.pivot_table(values=[\"ntl_mean\"], columns=[\"NAME_2\"], index=\"date\")\n", - "NTL = NTL.groupby(pd.Grouper(freq=\"W\")).mean()\n", + "df = dd.read_csv(\n", + " \"data/pse2_VNP46A2*.csv\",\n", + " parse_dates=[\"date\"],\n", + ").compute()\n", + "df = df[df[\"NAME_1\"] == \"Gaza\"]" + ] + }, + { + "cell_type": "markdown", + "id": "1914dfd0-3ce0-4ea2-9f59-f4f2e4f6aeb8", + "metadata": {}, + "source": [ + "## Methodology\n", "\n", - "NTL = NTL[(NTL.index >= \"2022-01-01\")]\n", + "Creating a time series of weekly radiance using NASA's Black Marble data involves several steps, including data acquisition, pre-processing, zonal statistics calculation, and time series generation. Below is a general methodology for this process." + ] + }, + { + "cell_type": "markdown", + "id": "bddb3c39-327a-4aab-b6a1-db0cdefd0f4c", + "metadata": {}, + "source": [ + "### Time Series Generation\n", "\n", - "data = 100*(NTL/NTL[(NTL.index >= \"2022-01-01\") & (NTL.index < \"2023-01-01\")].mean() - 1)" + "Organize the zonal statistics results in a tabular format, where each columnn corresponds to a specific zone, and rows represent the daily radiance values. Next, we aggregate the data on a weekly basis, computing the desired statistical metric (e.g., mean radiance) for each zone for each week. Finally, we will visualize the time series data to observe trends, patterns, and anomalies over time." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "068a1939-3562-42e0-abd7-51734893f722", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ntl_mean
NAME_2Deir Al-BalahGazaGaza ash ShamaliyahKhan YunisRafah
date
2021-12-2680.876536135.91113278.008197121.089891NaN
2022-01-02133.175823203.156980118.514480135.848382225.478376
2022-01-09120.802306154.040451108.742094129.915630218.354316
2022-01-16134.793033197.818275131.700803123.835625201.245460
2022-01-2397.599245151.64174094.552836108.125086165.917218
..................
2023-10-0197.974453189.140499145.523352104.302415189.881470
2023-10-0818.60169433.38744820.32008226.54702752.033815
2023-10-1515.49302031.56600822.92432818.40129748.671099
2023-10-2219.40677861.44968830.70980127.21503658.474931
2023-10-2921.13976571.70391439.98344329.83853561.242155
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ntl_mean
NAME_2Deir Al-BalahGazaGaza ash ShamaliyahKhan YunisRafah
date
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2022-01-023.403392-7.869988-18.3091775.24953411.442919
2022-01-09-6.203935-30.143928-25.0451830.6530917.921845
2022-01-164.659063-10.291046-9.219980-4.057438-0.534224
2022-01-23-24.219781-31.231724-34.825694-16.229294-17.995245
..................
2023-10-01-23.928454-14.2263460.307762-19.190937-6.150888
2023-10-08-85.556851-84.859068-85.993575-79.432495-74.282234
2023-10-15-87.970558-85.685076-84.198495-85.743460-75.944259
2023-10-22-84.931750-72.133074-78.832047-78.914951-71.098705
2023-10-29-83.586185-67.482867-72.439821-76.882375-69.731002
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97 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " ntl_mean \n", + "NAME_2 Deir Al-Balah Gaza Gaza ash Shamaliyah Khan Yunis Rafah\n", + "date \n", + "2021-12-26 -37.204006 -38.365424 -46.229746 -6.184716 NaN\n", + "2022-01-02 3.403392 -7.869988 -18.309177 5.249534 11.442919\n", + "2022-01-09 -6.203935 -30.143928 -25.045183 0.653091 7.921845\n", + "2022-01-16 4.659063 -10.291046 -9.219980 -4.057438 -0.534224\n", + "2022-01-23 -24.219781 -31.231724 -34.825694 -16.229294 -17.995245\n", + "... ... ... ... ... ...\n", + "2023-10-01 -23.928454 -14.226346 0.307762 -19.190937 -6.150888\n", + "2023-10-08 -85.556851 -84.859068 -85.993575 -79.432495 -74.282234\n", + "2023-10-15 -87.970558 -85.685076 -84.198495 -85.743460 -75.944259\n", + "2023-10-22 -84.931750 -72.133074 -78.832047 -78.914951 -71.098705\n", + "2023-10-29 -83.586185 -67.482867 -72.439821 -76.882375 -69.731002\n", + "\n", + "[97 rows x 5 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = 100 * (\n", + " NTL / NTL[(NTL.index >= \"2022-01-01\") & (NTL.index < \"2023-01-01\")].mean() - 1\n", + ")\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d978ff3c-25a1-4be3-9020-bc7ae6ad376c", "metadata": { "editable": true, "slideshow": { @@ -149,31 +796,31 @@ "source": [ "def create_plot(data):\n", " p = figure(\n", - " title=\"Palestine: Percent Change in Nighttime Lights Average Luminosity\",\n", + " title=\"Palestine: Percent Change in Nighttime Lights Radiance Average\",\n", " width=800,\n", " height=800,\n", " x_axis_label=\"Date\",\n", " x_axis_type=\"datetime\",\n", - " y_axis_label=\"NTL Luminosity Percent change (%)\",\n", + " y_axis_label=\"NTL Radiance Percent change (%)\",\n", " tools=\"pan,wheel_zoom,box_zoom,reset,save,box_select\",\n", " )\n", - " p.y_range = Range1d(-100, 100, bounds=(-100, 100))\n", - " p.xaxis.major_label_orientation = math.pi/4\n", + " p.y_range = Range1d(-100, 50, bounds=(-100, 100))\n", + " p.xaxis.major_label_orientation = math.pi / 4\n", " p.add_layout(\n", - " Title(\n", - " text=f\"Percent change (compared to 2022) in NTL luminosity for each second-level administrative division\",\n", - " text_font_size=\"12pt\",\n", - " text_font_style=\"italic\",\n", - " ),\n", - " \"above\",\n", + " Title(\n", + " text=f\"Percent change in NTL radiance for each second-level administrative division\",\n", + " text_font_size=\"12pt\",\n", + " text_font_style=\"italic\",\n", + " ),\n", + " \"above\",\n", " )\n", " p.add_layout(\n", - " Title(\n", - " text=f\"Source: NASA Black Marble. Creation date: {datetime.today().strftime('%d %B %Y')}. Feedback: datalab@worldbank.org.\",\n", - " text_font_size=\"10pt\",\n", - " text_font_style=\"italic\",\n", - " ),\n", - " \"below\",\n", + " Title(\n", + " text=f\"Source: NASA Black Marble. Creation date: {datetime.today().strftime('%d %B %Y')}. Feedback: datalab@worldbank.org.\",\n", + " text_font_size=\"10pt\",\n", + " text_font_style=\"italic\",\n", + " ),\n", + " \"below\",\n", " )\n", " p.add_layout(Legend(), \"right\")\n", " p.renderers.extend(\n", @@ -181,7 +828,7 @@ " Span(\n", " location=datetime(2023, 10, 7),\n", " dimension=\"height\",\n", - " line_color=\"red\",\n", + " line_color=\"gray\",\n", " line_width=1.5,\n", " line_dash=(4, 4),\n", " ),\n", @@ -189,31 +836,31 @@ " )\n", " p.add_tools(\n", " HoverTool(\n", - " tooltips=\"date: @x{%F}, percent change: @y\",\n", + " tooltips=\"Date: @x{%F}, Percent Change: @y{0.0}%\",\n", " formatters={\"@x\": \"datetime\"},\n", " )\n", " )\n", " renderers = []\n", - " for column, color in zip(data.columns, COLORS):\n", - " r = p.line(\n", - " data.index,\n", - " data[column],\n", - " legend_label=str(column[1]),\n", - " line_color=color,\n", - " line_width=2,\n", - " )\n", - " renderers.append(r)\n", + " for column, color in zip(data.columns, cc.b_glasbey_category10):\n", + " r = p.line(\n", + " data.index,\n", + " data[column],\n", + " legend_label=str(column[1]),\n", + " line_color=color,\n", + " line_width=2,\n", + " )\n", + " renderers.append(r)\n", "\n", " p.legend.location = \"bottom_left\"\n", " p.legend.click_policy = \"hide\"\n", " p.title.text_font_size = \"12pt\"\n", - " #p.sizing_mode = \"scale_both\"\n", + " # p.sizing_mode = \"scale_both\"\n", " return p" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "id": "c686bb4a-8fc0-4654-96e5-9babad8e388b", "metadata": { "editable": true, @@ -238,7 +885,7 @@ " \n", "
\n", " \n", - " Loading BokehJS ...\n", + " Loading BokehJS ...\n", "
\n" ] }, @@ -410,7 +1057,7 @@ " \"\"}};\n", "\n", " function display_loaded() {\n", - " const el = document.getElementById(\"b2e4a99b-7b90-4a33-b6ee-2d00d5b5b519\");\n", + " const el = document.getElementById(\"f3fe7cf1-cd0e-4295-a2c8-8a6fa15d7ff8\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", @@ -516,7 +1163,7 @@ " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", - " const cell = $(document.getElementById(\"b2e4a99b-7b90-4a33-b6ee-2d00d5b5b519\")).parents('.cell').data().cell;\n", + " const cell = $(document.getElementById(\"f3fe7cf1-cd0e-4295-a2c8-8a6fa15d7ff8\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " }\n", @@ -532,7 +1179,7 @@ " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"b2e4a99b-7b90-4a33-b6ee-2d00d5b5b519\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.0.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"b2e4a99b-7b90-4a33-b6ee-2d00d5b5b519\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"f3fe7cf1-cd0e-4295-a2c8-8a6fa15d7ff8\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.0.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"f3fe7cf1-cd0e-4295-a2c8-8a6fa15d7ff8\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -541,7 +1188,7 @@ "data": { "text/html": [ "\n", - "
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NASA Black Marble. Creation date: 13 November 2023. Feedback: datalab@worldbank.org.\",\"text_font_size\":\"10pt\",\"text_font_style\":\"italic\"}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1029\",\"attributes\":{\"axis\":{\"id\":\"p1013\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1034\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1030\"}}}]}}]}};\n", + " const render_items = [{\"docid\":\"779c1eb3-d914-4ffd-bde2-12b8382579e4\",\"roots\":{\"p1001\":\"af84b215-6555-4867-8fd9-dc959ea2cb7d\"},\"root_ids\":[\"p1001\"]}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", " }\n", " if (root.Bokeh !== undefined) {\n", @@ -590,9 +1237,28 @@ "show(create_plot(data))" ] }, + { + "cell_type": "markdown", + "id": "dff44f8a-4369-48d9-aedd-cdce371e2c29", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "```{figure} ../../docs/images/logo.png\n", + "---\n", + "height: 0px\n", + "---\n", + "Percent change (compared to 2022) in NTL radiance (NASA Black Marble VNP46A2) for each second-level administrative division. \n", + "```" + ] + }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "id": "fd00e8bd-e588-4204-91c0-44fb26d9e6c1", "metadata": { "editable": true, @@ -605,18 +1271,18 @@ }, "outputs": [], "source": [ - "def create_plot(data, COLORS):\n", + "def create_plot(data, colors):\n", " p = figure(\n", " title=data.columns[0][1],\n", " width=800,\n", - " height=800,\n", + " height=600,\n", " x_axis_label=\"Date\",\n", " x_axis_type=\"datetime\",\n", - " y_axis_label=\"NTL Luminosity Percent change (%)\",\n", + " y_axis_label=\"NTL Radiance Percent Change (%)\",\n", " tools=\"pan,wheel_zoom,box_zoom,reset,save,box_select\",\n", " )\n", - " p.y_range = Range1d(-100, 100, bounds=(-100, 100))\n", - " p.xaxis.major_label_orientation = math.pi/4\n", + " p.y_range = Range1d(-100, 50, bounds=(-100, 100))\n", + " p.xaxis.major_label_orientation = math.pi / 4\n", " p.add_layout(Legend(), \"right\")\n", " p.renderers.extend(\n", " [\n", @@ -631,19 +1297,19 @@ " )\n", " p.add_tools(\n", " HoverTool(\n", - " tooltips=\"date: @x{%F}, percent change: @y\",\n", + " tooltips=\"Date: @x{%F}, Percent Change: @y{0.0}%\",\n", " formatters={\"@x\": \"datetime\"},\n", " )\n", " )\n", " renderers = []\n", - " for column, color in zip(data.columns, COLORS):\n", - " r = p.line(\n", - " data.index,\n", - " data[column],\n", - " line_color=color,\n", - " line_width=2,\n", - " )\n", - " renderers.append(r)\n", + " for column, color in zip(data.columns, colors):\n", + " r = p.line(\n", + " data.index,\n", + " data[column],\n", + " line_color=color,\n", + " line_width=2,\n", + " )\n", + " renderers.append(r)\n", "\n", " p.legend.location = \"bottom_left\"\n", " p.legend.click_policy = \"hide\"\n", @@ -654,7 +1320,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "77172f27-42f2-4455-baeb-11c348a90017", "metadata": { "editable": true, @@ -669,20 +1335,21 @@ "source": [ "plots = list()\n", "\n", - "for column, color in zip(data.columns, COLORS):\n", + "for column, color in zip(data.columns, cc.b_glasbey_category10):\n", " p = create_plot(data[column].to_frame(), [color])\n", " plots.append(p)\n", "\n", - "p = gridplot(group(plots, 4))\n", - "p.sizing_mode = 'scale_both'" + "p = gridplot(group(plots, 2))\n", + "p.sizing_mode = \"scale_both\"" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "id": "4e168426-c3c9-4603-a080-a8d506609416", "metadata": { "editable": true, + "scrolled": true, "slideshow": { "slide_type": "" }, @@ -695,7 +1362,7 @@ "data": { "text/html": [ "\n", - "
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+ " const render_items = [{\"docid\":\"e3f795a2-1932-4667-aa13-02c833fc23eb\",\"roots\":{\"p1392\":\"aba136ee-47ab-4a73-9a4d-22c0a8788f9f\"},\"root_ids\":[\"p1392\"]}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", " }\n", " if (root.Bokeh !== undefined) {\n", @@ -733,7 +1400,7 @@ }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "id": "p2129" + "id": "p1392" } }, "output_type": "display_data" @@ -743,6 +1410,25 @@ "show(p)" ] }, + { + "cell_type": "markdown", + "id": "82f60a1c-f9de-4e17-857b-5406864195ea", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "```{figure} ../../docs/images/logo.png\n", + "---\n", + "height: 0px\n", + "---\n", + "Percent change (compared to 2022) in NTL radiance (NASA Black Marble VNP46A2) for each second-level administrative division. \n", + "```" + ] + }, { "cell_type": "markdown", "id": "9736dd3c-98cf-4f2c-a756-38babaa16562", @@ -759,7 +1445,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "id": "42b42c88-41b3-4a53-b7af-2f46759ec6d2", "metadata": { "editable": true, @@ -773,12 +1459,13 @@ "outputs": [], "source": [ "df = data.stack().reset_index()\n", - "df[\"date\"] = df[\"date\"].astype(str)" + "df[\"date\"] = df[\"date\"].astype(str)\n", + "df[\"NAME_2\"] = df[\"NAME_2\"].astype(str)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "id": "2d0502be-f123-419b-9360-cb89cc6a99e1", "metadata": { "editable": true, @@ -791,20 +1478,20 @@ }, "outputs": [], "source": [ - "# this is the colormap from the original NYTimes plot\n", - "#colors = [\"#75968f\", \"#a5bab7\", \"#c9d9d3\", \"#e2e2e2\", \"#dfccce\", \"#ddb7b1\", \"#cc7878\", \"#933b41\", \"#550b1d\"]\n", - "#colors = colors[::-1]\n", - "\n", "from palettable.colorbrewer.diverging import RdBu_7 as palette\n", "colors = palette.hex_colors\n", "\n", - "TOOLS = \"hover,save,pan,box_zoom,reset,wheel_zoom\"\n", - "\n", - "p = figure(title=f\"Palestine: Weekly Percent Change (compared to 2022 average) in Nighttime Lights Luminosity\",\n", - " x_range=df[\"date\"].unique(), y_range=df[\"NAME_2\"].unique(),\n", - " x_axis_location=\"above\", width=1800, height=800,\n", - " tools=TOOLS, toolbar_location='below',\n", - " tooltips=[('date', '@date @NAME_2'), ('rate', '@ntl_mean%')])\n", + "p = figure(\n", + " title=f\"Palestine: Weekly Percent Change (compared to 2022 average) in Nighttime Lights Radiance\",\n", + " x_range=df[\"date\"].unique(),\n", + " y_range=df[\"NAME_2\"].unique(),\n", + " x_axis_location=\"above\",\n", + " width=1800,\n", + " height=800,\n", + " tools=\"hover,save,pan,box_zoom,reset,wheel_zoom\",\n", + " toolbar_location=\"below\",\n", + " tooltips=[(\"date\", \"@date @NAME_2\"), (\"rate\", \"@ntl_mean%\")],\n", + ")\n", "\n", "p.title.text_font_size = \"18pt\"\n", "p.grid.grid_line_color = None\n", @@ -814,31 +1501,40 @@ "p.axis.major_label_standoff = 0\n", "p.xaxis.major_label_orientation = pi / 3\n", "\n", - "r = p.rect(x=\"date\", y=\"NAME_2\", width=1, height=1, source=df,\n", - " fill_color=linear_cmap(\"ntl_mean\", colors, low=-75, high=75),\n", - " line_color=None)\n", + "r = p.rect(\n", + " x=\"date\",\n", + " y=\"NAME_2\",\n", + " width=1,\n", + " height=1,\n", + " source=df,\n", + " fill_color=linear_cmap(\"ntl_mean\", colors, low=-75, high=75),\n", + " line_color=None,\n", + ")\n", "\n", "p.add_layout(\n", - " Title(\n", - " text=f\"Source: NASA Black Marble. Creation date: {datetime.today().strftime('%d %B %Y')}. Feedback: datalab@worldbank.org.\",\n", - " text_font_size=\"10pt\",\n", - " text_font_style=\"italic\",\n", - " ),\n", - " \"below\",\n", - " )\n", - "p.add_layout(r.construct_color_bar(\n", - " major_label_text_font_size=\"16px\",\n", - " ticker=BasicTicker(desired_num_ticks=len(colors)),\n", - " formatter=PrintfTickFormatter(format=\"%d%%\"),\n", - " label_standoff=6,\n", - " border_line_color=None,\n", - " padding=50,\n", - "), 'right')" + " Title(\n", + " text=f\"Source: NASA Black Marble. Creation date: {datetime.today().strftime('%d %B %Y')}. Feedback: datalab@worldbank.org.\",\n", + " text_font_size=\"10pt\",\n", + " text_font_style=\"italic\",\n", + " ),\n", + " \"below\",\n", + ")\n", + "p.add_layout(\n", + " r.construct_color_bar(\n", + " major_label_text_font_size=\"16px\",\n", + " ticker=BasicTicker(desired_num_ticks=len(colors)),\n", + " formatter=PrintfTickFormatter(format=\"%d%%\"),\n", + " label_standoff=6,\n", + " border_line_color=None,\n", + " padding=50,\n", + " ),\n", + " \"right\",\n", + ")" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "id": "82d8b8a1-9499-4231-a64c-178b4cf32c06", "metadata": { "editable": true, @@ -855,7 +1551,7 @@ "data": { "text/html": [ "\n", - "
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\"},\"shape\":[1504],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p2205\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p2206\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Rect\",\"id\":\"p2201\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"date\"},\"y\":{\"type\":\"field\",\"field\":\"NAME_2\"},\"width\":{\"type\":\"value\",\"value\":1},\"height\":{\"type\":\"value\",\"value\":1},\"line_color\":{\"type\":\"value\",\"value\":null},\"fill_color\":{\"type\":\"field\",\"field\":\"ntl_mean\",\"transform\":{\"type\":\"object\",\"name\":\"LinearColorMapper\",\"id\":\"p2194\",\"attributes\":{\"palette\":[\"#B2182B\",\"#EF8A62\",\"#FDDBC7\",\"#F7F7F7\",\"#D1E5F0\",\"#67A9CF\",\"#2166AC\"],\"low\":-75,\"high\":75}}}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Rect\",\"id\":\"p2202\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"date\"},\"y\":{\"type\":\"field\",\"field\":\"NAME_2\"},\"width\":{\"type\":\"value\",\"value\":1},\"height\":{\"type\":\"value\",\"value\":1},\"line_color\":{\"type\":\"value\",\"value\":null},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"field\",\"field\":\"ntl_mean\",\"transform\":{\"id\":\"p2194\"}},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Rect\",\"id\":\"p2203\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"date\"},\"y\":{\"type\":\"field\",\"field\":\"NAME_2\"},\"width\":{\"type\":\"value\",\"value\":1},\"height\":{\"type\":\"value\",\"value\":1},\"line_color\":{\"type\":\"value\",\"value\":null},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"field\",\"field\":\"ntl_mean\",\"transform\":{\"id\":\"p2194\"}},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p2172\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p2187\",\"attributes\":{\"renderers\":\"auto\",\"tooltips\":[[\"date\",\"@date @NAME_2\"],[\"rate\",\"@ntl_mean%\"]]}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p2188\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2189\"},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p2190\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p2191\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p2192\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2193\",\"attributes\":{\"renderers\":\"auto\"}}]}},\"toolbar_location\":\"below\",\"left\":[{\"type\":\"object\",\"name\":\"CategoricalAxis\",\"id\":\"p2182\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"CategoricalTicker\",\"id\":\"p2183\"},\"formatter\":{\"type\":\"object\",\"name\":\"CategoricalTickFormatter\",\"id\":\"p2184\"},\"major_label_standoff\":0,\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p2185\"},\"major_label_text_font_size\":\"16px\",\"axis_line_color\":null,\"major_tick_line_color\":null}}],\"right\":[{\"type\":\"object\",\"name\":\"ColorBar\",\"id\":\"p2210\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p2208\",\"attributes\":{\"desired_num_ticks\":7,\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"PrintfTickFormatter\",\"id\":\"p2209\",\"attributes\":{\"format\":\"%d%%\"}},\"major_label_policy\":{\"type\":\"object\",\"name\":\"NoOverlap\",\"id\":\"p2211\"},\"padding\":50,\"major_label_text_font_size\":\"16px\",\"label_standoff\":6,\"color_mapper\":{\"id\":\"p2194\"}}}],\"above\":[{\"type\":\"object\",\"name\":\"CategoricalAxis\",\"id\":\"p2177\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"CategoricalTicker\",\"id\":\"p2178\"},\"formatter\":{\"type\":\"object\",\"name\":\"CategoricalTickFormatter\",\"id\":\"p2179\"},\"major_label_standoff\":0,\"major_label_orientation\":1.0471975511965976,\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p2180\"},\"major_label_text_font_size\":\"16px\",\"axis_line_color\":null,\"major_tick_line_color\":null}}],\"below\":[{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p2207\",\"attributes\":{\"text\":\"Source: NASA Black Marble. Creation date: 31 October 2023. Feedback: datalab@worldbank.org.\",\"text_font_size\":\"10pt\",\"text_font_style\":\"italic\"}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p2181\",\"attributes\":{\"axis\":{\"id\":\"p2177\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p2186\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p2182\"},\"grid_line_color\":null}}]}}]}};\n", - " const render_items = [{\"docid\":\"2fb8813f-2ce2-415e-b6c6-4411405bafcc\",\"roots\":{\"p2163\":\"ed41429e-dcbb-461b-a035-632d18bf12f7\"},\"root_ids\":[\"p2163\"]}];\n", + " const docs_json = {\"42b7eebc-f030-4394-9b2d-535d6e49af7c\":{\"version\":\"3.3.0\",\"title\":\"Bokeh 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NASA Black Marble. Creation date: 13 November 2023. Feedback: datalab@worldbank.org.\",\"text_font_size\":\"10pt\",\"text_font_style\":\"italic\"}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1422\",\"attributes\":{\"axis\":{\"id\":\"p1418\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1427\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1423\"},\"grid_line_color\":null}}]}}]}};\n", + " const render_items = [{\"docid\":\"42b7eebc-f030-4394-9b2d-535d6e49af7c\",\"roots\":{\"p1404\":\"d815d20d-436c-442e-88e4-5a9f8adeb2fc\"},\"root_ids\":[\"p1404\"]}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", " }\n", " if (root.Bokeh !== undefined) {\n", @@ -893,7 +1589,7 @@ }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "id": "p2163" + "id": "p1404" } }, "output_type": "display_data" @@ -927,44 +1623,20 @@ "```\n", "\n", "#### Weekly\n", - "We visualize below weekly snapshots of the percent change (compared to 2022) in NTL average luminosity for each second-level administrative division. " - ] - }, - { - "cell_type": "markdown", - "id": "5c7979d4-72ea-4ee1-b268-d53ee9f2f99e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "remove-cell" - ] - }, - "source": [ - "PSE = geopandas.read_file(\"../../data/boundaries/gadm41_PSE_shp/gadm41_PSE_2.shp\")" - ] - }, - { - "cell_type": "markdown", - "id": "14eb950c-7b9c-4e02-aeb9-0eaa431713e8", - "metadata": {}, - "source": [ - "##### Week of 2023-10-08" + "We visualize below weekly snapshots of the percent change (compared to 2022) in NTL radiance average for each second-level administrative division. " ] }, { "cell_type": "code", - "execution_count": 13, - "id": "906501f3-d571-4c8c-bf82-9ded4f1ac631", + "execution_count": 16, + "id": "40a89914-7f7e-4744-a2d4-b9595995ea28", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ - "hide-input" + "remove-input" ] }, "outputs": [ @@ -998,7 +1670,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_be706a19a147631b65eae058ac753101 {\n", + " #map_809b3a9c324ee2b3eecd1176386fb42a {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1028,126 +1700,105 @@ "</head>\n", "<body>\n", " \n", + " <h3 align="center" style="font-size:20px">Week of <b>2022-01-02</b></h3>\n", " \n", - " <div class="folium-map" id="map_be706a19a147631b65eae058ac753101" ></div>\n", + " <div class="folium-map" id="map_809b3a9c324ee2b3eecd1176386fb42a" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var 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href=\\"http://www.openstreetmap.org/copyright\\"\\u003eODbL\\u003c/a\\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", - " ).addTo(map_be706a19a147631b65eae058ac753101);\n", + " ).addTo(map_809b3a9c324ee2b3eecd1176386fb42a);\n", " \n", " \n", - " map_be706a19a147631b65eae058ac753101.fitBounds(\n", - " [[31.223606110000105, 34.22902679400016], [32.55156707800006, 35.57545471200007]],\n", + " map_809b3a9c324ee2b3eecd1176386fb42a.fitBounds(\n", + " [[31.223606110000105, 34.22902679400016], [31.594427109000094, 34.54627227800012]],\n", " {}\n", " );\n", " \n", " \n", - " function geo_json_dd733d6aeacdc0b60613927b7166ec61_styler(feature) {\n", + " function geo_json_076320ad52ebdbaac92527b80f2f3b63_styler(feature) {\n", " switch(feature.id) {\n", - " case "0": \n", - " return {"color": "#ff8585", "fillColor": "#ff8585", "fillOpacity": 0.5, "weight": 2};\n", - " case "2": case "10": \n", - " return {"color": "#fdfdff", "fillColor": "#fdfdff", "fillOpacity": 0.5, "weight": 2};\n", - " case "3": \n", - " return {"color": "#ffa1a1", "fillColor": "#ffa1a1", "fillOpacity": 0.5, "weight": 2};\n", - " case "4": \n", - " return {"color": "#ffe1e1", "fillColor": "#ffe1e1", "fillOpacity": 0.5, "weight": 2};\n", - " case "6": \n", - " return {"color": "#8d8dff", "fillColor": "#8d8dff", "fillOpacity": 0.5, "weight": 2};\n", - " case "7": \n", - " return {"color": "#adadff", "fillColor": "#adadff", "fillOpacity": 0.5, "weight": 2};\n", - " case "8": \n", - " return {"color": "#8585ff", "fillColor": "#8585ff", "fillOpacity": 0.5, "weight": 2};\n", - " case "9": \n", - " return {"color": "#ffa5a5", "fillColor": "#ffa5a5", "fillOpacity": 0.5, "weight": 2};\n", - " case "11": \n", - " return {"color": "#000077", "fillColor": "#000077", "fillOpacity": 0.5, "weight": 2};\n", - " case "12": \n", - " return {"color": "#e9e9ff", "fillColor": "#e9e9ff", "fillOpacity": 0.5, "weight": 2};\n", - " case "13": \n", - " return {"color": "#ededff", "fillColor": "#ededff", "fillOpacity": 0.5, "weight": 2};\n", - " case "14": \n", - " return {"color": "#1111ff", "fillColor": "#1111ff", "fillOpacity": 0.5, "weight": 2};\n", - " case "15": \n", - " return {"color": "#b9b9ff", "fillColor": "#b9b9ff", "fillOpacity": 0.5, "weight": 2};\n", + " case "1": \n", + " return {"color": "#cae11f", "fillColor": "#cae11f", "fillOpacity": 0.5, "weight": 2};\n", + " case "2": \n", + " return {"color": "#86d549", "fillColor": "#86d549", "fillOpacity": 0.5, "weight": 2};\n", " default:\n", - " return {"color": "#ffb9b9", "fillColor": "#ffb9b9", "fillOpacity": 0.5, "weight": 2};\n", + " return {"color": "#fde725", "fillColor": "#fde725", "fillOpacity": 0.5, "weight": 2};\n", " }\n", " }\n", - " function geo_json_dd733d6aeacdc0b60613927b7166ec61_highlighter(feature) {\n", + " function geo_json_076320ad52ebdbaac92527b80f2f3b63_highlighter(feature) {\n", " switch(feature.id) {\n", " default:\n", " return {"fillOpacity": 0.75};\n", " }\n", " }\n", - " function geo_json_dd733d6aeacdc0b60613927b7166ec61_pointToLayer(feature, latlng) {\n", + " function geo_json_076320ad52ebdbaac92527b80f2f3b63_pointToLayer(feature, latlng) {\n", " var opts = {"bubblingMouseEvents": true, "color": "#3388ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3388ff", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 2, "stroke": true, "weight": 3};\n", " \n", - " let style = geo_json_dd733d6aeacdc0b60613927b7166ec61_styler(feature)\n", + " let style = geo_json_076320ad52ebdbaac92527b80f2f3b63_styler(feature)\n", " Object.assign(opts, style)\n", " \n", " return new L.CircleMarker(latlng, opts)\n", " }\n", "\n", - " function geo_json_dd733d6aeacdc0b60613927b7166ec61_onEachFeature(feature, layer) {\n", + " function geo_json_076320ad52ebdbaac92527b80f2f3b63_onEachFeature(feature, layer) {\n", " layer.on({\n", " mouseout: function(e) {\n", " if(typeof e.target.setStyle === "function"){\n", - " geo_json_dd733d6aeacdc0b60613927b7166ec61.resetStyle(e.target);\n", + " geo_json_076320ad52ebdbaac92527b80f2f3b63.resetStyle(e.target);\n", " }\n", " },\n", " mouseover: function(e) {\n", " if(typeof e.target.setStyle === "function"){\n", - " const highlightStyle = geo_json_dd733d6aeacdc0b60613927b7166ec61_highlighter(e.target.feature)\n", + " const highlightStyle = geo_json_076320ad52ebdbaac92527b80f2f3b63_highlighter(e.target.feature)\n", " e.target.setStyle(highlightStyle);\n", " }\n", " },\n", " });\n", " };\n", - " var geo_json_dd733d6aeacdc0b60613927b7166ec61 = L.geoJson(null, {\n", - " onEachFeature: geo_json_dd733d6aeacdc0b60613927b7166ec61_onEachFeature,\n", + " var geo_json_076320ad52ebdbaac92527b80f2f3b63 = L.geoJson(null, {\n", + " onEachFeature: geo_json_076320ad52ebdbaac92527b80f2f3b63_onEachFeature,\n", " \n", - " style: geo_json_dd733d6aeacdc0b60613927b7166ec61_styler,\n", - " pointToLayer: geo_json_dd733d6aeacdc0b60613927b7166ec61_pointToLayer\n", + " style: geo_json_076320ad52ebdbaac92527b80f2f3b63_styler,\n", + " pointToLayer: geo_json_076320ad52ebdbaac92527b80f2f3b63_pointToLayer\n", " });\n", "\n", - " function geo_json_dd733d6aeacdc0b60613927b7166ec61_add (data) {\n", - " geo_json_dd733d6aeacdc0b60613927b7166ec61\n", + " function geo_json_076320ad52ebdbaac92527b80f2f3b63_add (data) {\n", + " geo_json_076320ad52ebdbaac92527b80f2f3b63\n", " .addData(data)\n", - " .addTo(map_be706a19a147631b65eae058ac753101);\n", + " .addTo(map_809b3a9c324ee2b3eecd1176386fb42a);\n", " }\n", - " geo_json_dd733d6aeacdc0b60613927b7166ec61_add({"bbox": [34.22902679400016, 31.223606110000105, 35.57545471200007, 32.55156707800006], "features": [{"bbox": [34.30597305300017, 31.372600555000133, 34.415882110000155, 31.462638856000126], "geometry": {"coordinates": [[[34.415882110000155, 31.42720031800019], [34.40790176400009, 31.420608522000123], [34.37456893900003, 31.38384056100017], 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"VARNAME_2": "NA", "__folium_color": "#fde725", "level_0": "ntl_mean", "percent change in radiance average (%)": 11.442919387086302}, "type": "Feature"}], "type": "FeatureCollection"});\n", "\n", " \n", " \n", - " geo_json_dd733d6aeacdc0b60613927b7166ec61.bindTooltip(\n", + " geo_json_076320ad52ebdbaac92527b80f2f3b63.bindTooltip(\n", " function(layer){\n", " let div = L.DomUtil.create('div');\n", " \n", " let handleObject = feature=>typeof(feature)=='object' ? JSON.stringify(feature) : feature;\n", - " let fields = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "luminosity"];\n", - " let aliases = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "luminosity"];\n", + " let fields = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "percent change in radiance average (%)"];\n", + " let aliases = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "percent change in radiance average (%)"];\n", " let table = '<table>' +\n", " String(\n", " fields.map(\n", @@ -1165,42 +1816,42 @@ " ,{"className": "foliumtooltip", "sticky": true});\n", " \n", " \n", - " var color_map_31a2b5b5db19437d874807ebf6868043 = {};\n", + " var color_map_f2859cdadba538badc7dbf07d7d8688d = {};\n", "\n", " \n", - " color_map_31a2b5b5db19437d874807ebf6868043.color = d3.scale.threshold()\n", - " .domain([-100.0, -99.59919839679358, -99.19839679358718, -98.79759519038076, -98.39679358717434, -97.99599198396794, -97.59519038076152, -97.1943887775551, -96.7935871743487, -96.39278557114228, -95.99198396793587, -95.59118236472946, -95.19038076152304, -94.78957915831663, -94.38877755511022, -93.9879759519038, -93.58717434869739, -93.18637274549098, -92.78557114228457, -92.38476953907815, -91.98396793587175, -91.58316633266533, -91.18236472945893, -90.78156312625251, -90.38076152304609, -89.97995991983967, -89.57915831663327, -89.17835671342685, -88.77755511022045, -88.37675350701403, -87.97595190380761, -87.5751503006012, -87.17434869739479, -86.77354709418837, -86.37274549098197, -85.97194388777555, -85.57114228456913, -85.17034068136273, -84.76953907815631, 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color_map_31a2b5b5db19437d874807ebf6868043.legend.addTo(map_be706a19a147631b65eae058ac753101);\n", + " color_map_f2859cdadba538badc7dbf07d7d8688d.legend = L.control({position: 'topright'});\n", + " color_map_f2859cdadba538badc7dbf07d7d8688d.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", + " color_map_f2859cdadba538badc7dbf07d7d8688d.legend.addTo(map_809b3a9c324ee2b3eecd1176386fb42a);\n", "\n", - " color_map_31a2b5b5db19437d874807ebf6868043.xAxis = d3.svg.axis()\n", - " .scale(color_map_31a2b5b5db19437d874807ebf6868043.x)\n", + " color_map_f2859cdadba538badc7dbf07d7d8688d.xAxis = d3.svg.axis()\n", + " .scale(color_map_f2859cdadba538badc7dbf07d7d8688d.x)\n", " .orient("top")\n", " .tickSize(1)\n", - " .tickValues([-100.0, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', -79.6078431372549, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 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'', '', '', '', '', '', '', '', '', '', '', '', -8.235294117647058, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']);\n", "\n", - " color_map_31a2b5b5db19437d874807ebf6868043.svg = d3.select(".legend.leaflet-control").append("svg")\n", + " color_map_f2859cdadba538badc7dbf07d7d8688d.svg = d3.select(".legend.leaflet-control").append("svg")\n", " .attr("id", 'legend')\n", " .attr("width", 450)\n", " .attr("height", 40);\n", "\n", - " color_map_31a2b5b5db19437d874807ebf6868043.g = color_map_31a2b5b5db19437d874807ebf6868043.svg.append("g")\n", + " color_map_f2859cdadba538badc7dbf07d7d8688d.g = color_map_f2859cdadba538badc7dbf07d7d8688d.svg.append("g")\n", " .attr("class", "key")\n", " .attr("transform", "translate(25,16)");\n", "\n", - " color_map_31a2b5b5db19437d874807ebf6868043.g.selectAll("rect")\n", - " .data(color_map_31a2b5b5db19437d874807ebf6868043.color.range().map(function(d, i) {\n", + " color_map_f2859cdadba538badc7dbf07d7d8688d.g.selectAll("rect")\n", + " .data(color_map_f2859cdadba538badc7dbf07d7d8688d.color.range().map(function(d, i) {\n", " return {\n", - " x0: i ? color_map_31a2b5b5db19437d874807ebf6868043.x(color_map_31a2b5b5db19437d874807ebf6868043.color.domain()[i - 1]) : color_map_31a2b5b5db19437d874807ebf6868043.x.range()[0],\n", - " x1: i < color_map_31a2b5b5db19437d874807ebf6868043.color.domain().length ? color_map_31a2b5b5db19437d874807ebf6868043.x(color_map_31a2b5b5db19437d874807ebf6868043.color.domain()[i]) : color_map_31a2b5b5db19437d874807ebf6868043.x.range()[1],\n", + " x0: i ? color_map_f2859cdadba538badc7dbf07d7d8688d.x(color_map_f2859cdadba538badc7dbf07d7d8688d.color.domain()[i - 1]) : color_map_f2859cdadba538badc7dbf07d7d8688d.x.range()[0],\n", + " x1: i < color_map_f2859cdadba538badc7dbf07d7d8688d.color.domain().length ? color_map_f2859cdadba538badc7dbf07d7d8688d.x(color_map_f2859cdadba538badc7dbf07d7d8688d.color.domain()[i]) : color_map_f2859cdadba538badc7dbf07d7d8688d.x.range()[1],\n", " z: d\n", " };\n", " }))\n", @@ -1210,45 +1861,46 @@ " .attr("width", function(d) { return d.x1 - d.x0; })\n", " .style("fill", function(d) { return d.z; });\n", "\n", - " color_map_31a2b5b5db19437d874807ebf6868043.g.call(color_map_31a2b5b5db19437d874807ebf6868043.xAxis).append("text")\n", + " color_map_f2859cdadba538badc7dbf07d7d8688d.g.call(color_map_f2859cdadba538badc7dbf07d7d8688d.xAxis).append("text")\n", " .attr("class", "caption")\n", " .attr("y", 21)\n", - " .text("luminosity");\n", + " .text("percent change in radiance average (%)");\n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "PSE.merge(data.iloc[-3].to_frame(\"luminosity\").reset_index(), on=\"NAME_2\").explore(column=\"luminosity\", cmap=\"seismic_r\", vmin=-100, vmax=100)" - ] - }, - { - "cell_type": "markdown", - "id": "0f83db80-5842-4075-a4fd-29c01ea96dde", - "metadata": {}, - "source": [ - "##### Week of 2023-10-15" + "m = PSE.merge(\n", + " data.iloc[1].to_frame(\"percent change in radiance average (%)\").reset_index(),\n", + " on=\"NAME_2\",\n", + ").explore(\n", + " column=\"percent change in radiance average (%)\", cmap=\"viridis\", vmin=-100, vmax=0\n", + ")\n", + "\n", + "title_html = f\"\"\"

Week of {data.iloc[1].name.strftime(\"%F\")}

\"\"\"\n", + "m.get_root().html.add_child(folium.Element(title_html))\n", + "m" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "ecf04d86-b3bd-4b69-9801-4a12419e5534", + "execution_count": 17, + "id": "6bc5e49e-0d5a-4eae-8d7c-e3002420d897", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ - "hide-input" + "remove-input" ] }, "outputs": [ @@ -1282,7 +1934,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_9ee68f41dc3150d15490ad1f5ae271bd {\n", + " #map_dd93740f23ed7028ca4121ccae45f95c {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1312,126 +1964,109 @@ "</head>\n", "<body>\n", " \n", + " <h3 align="center" style="font-size:20px">Week of <b>2023-10-08</b></h3>\n", " \n", - " <div class="folium-map" id="map_9ee68f41dc3150d15490ad1f5ae271bd" ></div>\n", + " <div class="folium-map" id="map_dd93740f23ed7028ca4121ccae45f95c" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_9ee68f41dc3150d15490ad1f5ae271bd = L.map(\n", - " "map_9ee68f41dc3150d15490ad1f5ae271bd",\n", + " var map_dd93740f23ed7028ca4121ccae45f95c = L.map(\n", + " "map_dd93740f23ed7028ca4121ccae45f95c",\n", " {\n", - " center: [31.887586594000084, 34.902240753000115],\n", + " center: [31.4090166095001, 34.38764953600014],\n", " crs: L.CRS.EPSG3857,\n", " zoom: 10,\n", " zoomControl: true,\n", " preferCanvas: false,\n", " }\n", " );\n", - " L.control.scale().addTo(map_9ee68f41dc3150d15490ad1f5ae271bd);\n", + " L.control.scale().addTo(map_dd93740f23ed7028ca4121ccae45f95c);\n", "\n", " \n", "\n", " \n", " \n", - " var tile_layer_a0d4de82a01654df7cc62e4feac013bb = L.tileLayer(\n", + " var tile_layer_7f2e7bf8360f3812d7781d0f83b20e47 = L.tileLayer(\n", " "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "Data by \\u0026copy; \\u003ca target=\\"_blank\\" href=\\"http://openstreetmap.org\\"\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\"_blank\\" href=\\"http://www.openstreetmap.org/copyright\\"\\u003eODbL\\u003c/a\\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", - " ).addTo(map_9ee68f41dc3150d15490ad1f5ae271bd);\n", + " ).addTo(map_dd93740f23ed7028ca4121ccae45f95c);\n", " \n", " \n", - " map_9ee68f41dc3150d15490ad1f5ae271bd.fitBounds(\n", - " [[31.223606110000105, 34.22902679400016], [32.55156707800006, 35.57545471200007]],\n", + " map_dd93740f23ed7028ca4121ccae45f95c.fitBounds(\n", + " [[31.223606110000105, 34.22902679400016], [31.594427109000094, 34.54627227800012]],\n", " {}\n", " );\n", " \n", " \n", - " function geo_json_e7b459b5f7aec7d15068fc1310bc731f_styler(feature) {\n", + " function geo_json_5369e3296e58f264b1781ed55988e450_styler(feature) {\n", " switch(feature.id) {\n", - " case "0": case "2": \n", - " return {"color": "#a30000", "fillColor": "#a30000", "fillOpacity": 0.5, "weight": 2};\n", - " case "1": \n", - " return {"color": "#a60000", "fillColor": "#a60000", "fillOpacity": 0.5, "weight": 2};\n", + " case "0": \n", + " return {"color": "#46327e", "fillColor": "#46327e", "fillOpacity": 0.5, "weight": 2};\n", + " case "2": \n", + " return {"color": "#46307e", "fillColor": "#46307e", "fillOpacity": 0.5, "weight": 2};\n", " case "3": \n", - " return {"color": "#b30000", "fillColor": "#b30000", "fillOpacity": 0.5, "weight": 2};\n", + " return {"color": "#404588", "fillColor": "#404588", "fillOpacity": 0.5, "weight": 2};\n", " case "4": \n", - " return {"color": "#c00000", "fillColor": "#c00000", "fillOpacity": 0.5, "weight": 2};\n", - " case "5": \n", - " return {"color": "#ff7171", "fillColor": "#ff7171", "fillOpacity": 0.5, "weight": 2};\n", - " case "6": \n", - " return {"color": "#d9d9ff", "fillColor": "#d9d9ff", "fillOpacity": 0.5, "weight": 2};\n", - " case "7": \n", - " return {"color": "#adadff", "fillColor": "#adadff", "fillOpacity": 0.5, "weight": 2};\n", - " case "9": \n", - " return {"color": "#ffd5d5", "fillColor": "#ffd5d5", "fillOpacity": 0.5, "weight": 2};\n", - " case "10": \n", - " return {"color": "#e5e5ff", "fillColor": "#e5e5ff", "fillOpacity": 0.5, "weight": 2};\n", - " case "11": \n", - " return {"color": "#fdfdff", "fillColor": "#fdfdff", "fillOpacity": 0.5, "weight": 2};\n", - " case "12": \n", - " return {"color": "#d5d5ff", "fillColor": "#d5d5ff", "fillOpacity": 0.5, "weight": 2};\n", - " case "14": \n", - " return {"color": "#cdcdff", "fillColor": "#cdcdff", "fillOpacity": 0.5, "weight": 2};\n", - " case "15": \n", - " return {"color": "#ffd9d9", "fillColor": "#ffd9d9", "fillOpacity": 0.5, "weight": 2};\n", + " return {"color": "#3a538b", "fillColor": "#3a538b", "fillOpacity": 0.5, "weight": 2};\n", " default:\n", - " return {"color": "#e1e1ff", "fillColor": "#e1e1ff", "fillOpacity": 0.5, "weight": 2};\n", + " return {"color": "#463480", "fillColor": "#463480", "fillOpacity": 0.5, "weight": 2};\n", " }\n", " }\n", - " function geo_json_e7b459b5f7aec7d15068fc1310bc731f_highlighter(feature) {\n", + " function geo_json_5369e3296e58f264b1781ed55988e450_highlighter(feature) {\n", " switch(feature.id) {\n", " default:\n", " return {"fillOpacity": 0.75};\n", " }\n", " }\n", - " function geo_json_e7b459b5f7aec7d15068fc1310bc731f_pointToLayer(feature, latlng) {\n", + " function geo_json_5369e3296e58f264b1781ed55988e450_pointToLayer(feature, latlng) {\n", " var opts = {"bubblingMouseEvents": true, "color": "#3388ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3388ff", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 2, "stroke": true, "weight": 3};\n", " \n", - " let style = geo_json_e7b459b5f7aec7d15068fc1310bc731f_styler(feature)\n", + " let style = geo_json_5369e3296e58f264b1781ed55988e450_styler(feature)\n", " Object.assign(opts, style)\n", " \n", " return new L.CircleMarker(latlng, opts)\n", " }\n", "\n", - " function geo_json_e7b459b5f7aec7d15068fc1310bc731f_onEachFeature(feature, layer) {\n", + " function geo_json_5369e3296e58f264b1781ed55988e450_onEachFeature(feature, layer) {\n", " layer.on({\n", " mouseout: function(e) {\n", " if(typeof e.target.setStyle === "function"){\n", - " geo_json_e7b459b5f7aec7d15068fc1310bc731f.resetStyle(e.target);\n", + " geo_json_5369e3296e58f264b1781ed55988e450.resetStyle(e.target);\n", " }\n", " },\n", " mouseover: function(e) {\n", " if(typeof e.target.setStyle === "function"){\n", - " const highlightStyle = geo_json_e7b459b5f7aec7d15068fc1310bc731f_highlighter(e.target.feature)\n", + " const highlightStyle = geo_json_5369e3296e58f264b1781ed55988e450_highlighter(e.target.feature)\n", " e.target.setStyle(highlightStyle);\n", " }\n", " },\n", " });\n", " };\n", - " var geo_json_e7b459b5f7aec7d15068fc1310bc731f = L.geoJson(null, {\n", - " onEachFeature: geo_json_e7b459b5f7aec7d15068fc1310bc731f_onEachFeature,\n", + " var geo_json_5369e3296e58f264b1781ed55988e450 = L.geoJson(null, {\n", + " onEachFeature: geo_json_5369e3296e58f264b1781ed55988e450_onEachFeature,\n", " \n", - " style: geo_json_e7b459b5f7aec7d15068fc1310bc731f_styler,\n", - " pointToLayer: geo_json_e7b459b5f7aec7d15068fc1310bc731f_pointToLayer\n", + " style: geo_json_5369e3296e58f264b1781ed55988e450_styler,\n", + " pointToLayer: geo_json_5369e3296e58f264b1781ed55988e450_pointToLayer\n", " });\n", "\n", - " function geo_json_e7b459b5f7aec7d15068fc1310bc731f_add (data) {\n", - " geo_json_e7b459b5f7aec7d15068fc1310bc731f\n", + " function geo_json_5369e3296e58f264b1781ed55988e450_add (data) {\n", + " geo_json_5369e3296e58f264b1781ed55988e450\n", " .addData(data)\n", - " .addTo(map_9ee68f41dc3150d15490ad1f5ae271bd);\n", + " .addTo(map_dd93740f23ed7028ca4121ccae45f95c);\n", " }\n", - " geo_json_e7b459b5f7aec7d15068fc1310bc731f_add({"bbox": [34.22902679400016, 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31.372600555000133]]], "type": "Polygon"}, "id": "3", "properties": {"CC_2": "NA", "COUNTRY": "Palestine", "ENGTYPE_2": "Governorate", "GID_0": "PSE", "GID_1": "PSE.1_1", "GID_2": "PSE.1.4_1", "HASC_2": "PS.GZ.KY", "NAME_1": "Gaza", "NAME_2": "Khan Yunis", "NL_NAME_1": "NA", "NL_NAME_2": "NA", "TYPE_2": "Governorate", "VARNAME_2": "NA", "__folium_color": "#404588", "level_0": "ntl_mean", "percent change in radiance average (%)": -79.43249510728026}, "type": "Feature"}, {"bbox": [34.22902679400016, 31.223606110000105, 34.32059097300015, 31.33576202400002], "geometry": {"coordinates": [[[34.268009186000086, 31.223606110000105], [34.262340545000086, 31.239746094000054], [34.23046493400017, 31.330465317999995], [34.22902679400016, 31.33430671800005], [34.22930526700003, 31.33430671800005], [34.22930526700003, 31.334583283000143], [34.229862213000104, 31.334583283000143], [34.229862213000104, 31.33486175600018], [34.23014068600014, 31.33486175600018], [34.23014068600014, 31.335138322000148], [34.23041534400011, 31.335138322000148], [34.23041534400011, 31.335416795000185], [34.230693816999974, 31.335416795000185], [34.230693816999974, 31.335695267999995], [34.23125076300005, 31.33576202400002], [34.235092163000104, 31.334268570999996], [34.238101959999995, 31.333089828000084], [34.240829467000026, 31.331464767], [34.24997329600012, 31.325531007000052], [34.256732940000006, 31.32150840800017], [34.26201629500008, 31.317554473000143], [34.26743316700009, 31.314439774000107], [34.28264999400017, 31.30684089600004], [34.292926789000035, 31.30216026400018], [34.30240631000004, 31.298530579000158], [34.305870055000184, 31.296487808000165], [34.30770492600004, 31.294584274000044], [34.30912780799997, 31.291267396000023], [34.31140899700017, 31.28265762300009], [34.31308746300016, 31.27459716800007], [34.31459808400018, 31.27003288300017], [34.316905975000054, 31.265190124000128], [34.320079804000045, 31.25949287500009], [34.32059097300015, 31.25821685800014], [34.313014984999995, 31.252599717000123], [34.268009186000086, 31.223606110000105]]], "type": "Polygon"}, "id": "4", "properties": {"CC_2": "NA", "COUNTRY": "Palestine", "ENGTYPE_2": "Governorate", "GID_0": "PSE", "GID_1": "PSE.1_1", "GID_2": "PSE.1.5_1", "HASC_2": "PS.GZ.RA", "NAME_1": "Gaza", "NAME_2": "Rafah", "NL_NAME_1": "NA", "NL_NAME_2": "NA", "TYPE_2": "Governorate", "VARNAME_2": "NA", "__folium_color": "#3a538b", "level_0": "ntl_mean", "percent change in radiance average (%)": -74.28223355559753}, "type": "Feature"}], "type": "FeatureCollection"});\n", "\n", " \n", " \n", - " geo_json_e7b459b5f7aec7d15068fc1310bc731f.bindTooltip(\n", + " geo_json_5369e3296e58f264b1781ed55988e450.bindTooltip(\n", " function(layer){\n", " let div = L.DomUtil.create('div');\n", " \n", " let handleObject = feature=>typeof(feature)=='object' ? JSON.stringify(feature) : feature;\n", - " let fields = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "luminosity"];\n", - " let aliases = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "luminosity"];\n", + " let fields = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "percent change in radiance average (%)"];\n", + " let aliases = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "percent change in radiance average (%)"];\n", " let table = '<table>' +\n", " String(\n", " fields.map(\n", @@ -1449,42 +2084,42 @@ " ,{"className": "foliumtooltip", "sticky": true});\n", " \n", " \n", - " var color_map_423bd39b53afa1428b2a572f3b58e752 = {};\n", + " var color_map_e654b3f941521b4e6f4674cc5a12cd9c = {};\n", "\n", " \n", - " color_map_423bd39b53afa1428b2a572f3b58e752.color = d3.scale.threshold()\n", - " .domain([-100.0, -99.59919839679358, -99.19839679358718, -98.79759519038076, -98.39679358717434, -97.99599198396794, -97.59519038076152, -97.1943887775551, -96.7935871743487, -96.39278557114228, -95.99198396793587, -95.59118236472946, -95.19038076152304, -94.78957915831663, -94.38877755511022, -93.9879759519038, -93.58717434869739, -93.18637274549098, -92.78557114228457, -92.38476953907815, -91.98396793587175, -91.58316633266533, -91.18236472945893, -90.78156312625251, -90.38076152304609, -89.97995991983967, -89.57915831663327, -89.17835671342685, -88.77755511022045, -88.37675350701403, -87.97595190380761, -87.5751503006012, -87.17434869739479, -86.77354709418837, -86.37274549098197, -85.97194388777555, -85.57114228456913, -85.17034068136273, -84.76953907815631, 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color_map_423bd39b53afa1428b2a572f3b58e752.legend.addTo(map_9ee68f41dc3150d15490ad1f5ae271bd);\n", + " color_map_e654b3f941521b4e6f4674cc5a12cd9c.legend = L.control({position: 'topright'});\n", + " color_map_e654b3f941521b4e6f4674cc5a12cd9c.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", + " color_map_e654b3f941521b4e6f4674cc5a12cd9c.legend.addTo(map_dd93740f23ed7028ca4121ccae45f95c);\n", "\n", - " color_map_423bd39b53afa1428b2a572f3b58e752.xAxis = d3.svg.axis()\n", - " .scale(color_map_423bd39b53afa1428b2a572f3b58e752.x)\n", + " color_map_e654b3f941521b4e6f4674cc5a12cd9c.xAxis = d3.svg.axis()\n", + " .scale(color_map_e654b3f941521b4e6f4674cc5a12cd9c.x)\n", " .orient("top")\n", " .tickSize(1)\n", - " .tickValues([-100.0, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', -79.6078431372549, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 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color_map_e654b3f941521b4e6f4674cc5a12cd9c.g.selectAll("rect")\n", + " .data(color_map_e654b3f941521b4e6f4674cc5a12cd9c.color.range().map(function(d, i) {\n", " return {\n", - " x0: i ? color_map_423bd39b53afa1428b2a572f3b58e752.x(color_map_423bd39b53afa1428b2a572f3b58e752.color.domain()[i - 1]) : color_map_423bd39b53afa1428b2a572f3b58e752.x.range()[0],\n", - " x1: i < color_map_423bd39b53afa1428b2a572f3b58e752.color.domain().length ? color_map_423bd39b53afa1428b2a572f3b58e752.x(color_map_423bd39b53afa1428b2a572f3b58e752.color.domain()[i]) : color_map_423bd39b53afa1428b2a572f3b58e752.x.range()[1],\n", + " x0: i ? color_map_e654b3f941521b4e6f4674cc5a12cd9c.x(color_map_e654b3f941521b4e6f4674cc5a12cd9c.color.domain()[i - 1]) : color_map_e654b3f941521b4e6f4674cc5a12cd9c.x.range()[0],\n", + " x1: i < color_map_e654b3f941521b4e6f4674cc5a12cd9c.color.domain().length ? color_map_e654b3f941521b4e6f4674cc5a12cd9c.x(color_map_e654b3f941521b4e6f4674cc5a12cd9c.color.domain()[i]) : color_map_e654b3f941521b4e6f4674cc5a12cd9c.x.range()[1],\n", " z: d\n", " };\n", " }))\n", @@ -1494,45 +2129,46 @@ " .attr("width", function(d) { return d.x1 - d.x0; })\n", " .style("fill", function(d) { return d.z; });\n", "\n", - " color_map_423bd39b53afa1428b2a572f3b58e752.g.call(color_map_423bd39b53afa1428b2a572f3b58e752.xAxis).append("text")\n", + " color_map_e654b3f941521b4e6f4674cc5a12cd9c.g.call(color_map_e654b3f941521b4e6f4674cc5a12cd9c.xAxis).append("text")\n", " .attr("class", "caption")\n", " .attr("y", 21)\n", - " .text("luminosity");\n", + " .text("percent change in radiance average (%)");\n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "PSE.merge(data.iloc[-2].to_frame(\"luminosity\").reset_index(), on=\"NAME_2\").explore(column=\"luminosity\", cmap=\"seismic_r\", vmin=-100, vmax=100)" - ] - }, - { - "cell_type": "markdown", - "id": "5dec9be9-7a13-4eea-adb0-ba6fa43c492c", - "metadata": {}, - "source": [ - "##### Week of 2023-10-22" + "m = PSE.merge(\n", + " data.loc[\"2023-10-08\"].to_frame(\"percent change in radiance average (%)\").reset_index(),\n", + " on=\"NAME_2\",\n", + ").explore(\n", + " column=\"percent change in radiance average (%)\", cmap=\"viridis\", vmin=-100, vmax=0\n", + ")\n", + "\n", + "title_html = f\"\"\"

Week of {data.loc[\"2023-10-08\"].name.strftime(\"%F\")}

\"\"\"\n", + "m.get_root().html.add_child(folium.Element(title_html))\n", + "m" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "c4f44c62-ec47-4a8c-b2f4-d5330395d202", + "execution_count": 18, + "id": "716d106d-70d0-4594-81e8-b8d5d2655784", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ - "hide-input" + "remove-input" ] }, "outputs": [ @@ -1566,7 +2202,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_d7f5043427f8fef4e1b0aedd736d0fc1 {\n", + " #map_2248a8380b652ec76903b2c6f3af61c1 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1596,124 +2232,109 @@ "</head>\n", "<body>\n", " \n", + " <h3 align="center" style="font-size:20px">Week of <b>2023-10-29</b></h3>\n", " \n", - " <div class="folium-map" id="map_d7f5043427f8fef4e1b0aedd736d0fc1" ></div>\n", + " <div class="folium-map" id="map_2248a8380b652ec76903b2c6f3af61c1" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_d7f5043427f8fef4e1b0aedd736d0fc1 = L.map(\n", - " "map_d7f5043427f8fef4e1b0aedd736d0fc1",\n", + " var map_2248a8380b652ec76903b2c6f3af61c1 = L.map(\n", + " "map_2248a8380b652ec76903b2c6f3af61c1",\n", " {\n", - " center: [31.887586594000084, 34.902240753000115],\n", + " center: [31.4090166095001, 34.38764953600014],\n", " crs: L.CRS.EPSG3857,\n", " zoom: 10,\n", " zoomControl: true,\n", " preferCanvas: false,\n", " }\n", " );\n", - " L.control.scale().addTo(map_d7f5043427f8fef4e1b0aedd736d0fc1);\n", + " L.control.scale().addTo(map_2248a8380b652ec76903b2c6f3af61c1);\n", "\n", " \n", "\n", " \n", " \n", - " var tile_layer_db5f3a242bd1a2d3dc97fc5b04e74aa6 = L.tileLayer(\n", + " var tile_layer_e1bdfa93fb8794214c331bf15ffcc218 = L.tileLayer(\n", " "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "Data by \\u0026copy; \\u003ca target=\\"_blank\\" href=\\"http://openstreetmap.org\\"\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\"_blank\\" href=\\"http://www.openstreetmap.org/copyright\\"\\u003eODbL\\u003c/a\\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", - " ).addTo(map_d7f5043427f8fef4e1b0aedd736d0fc1);\n", + " ).addTo(map_2248a8380b652ec76903b2c6f3af61c1);\n", " \n", " \n", - " map_d7f5043427f8fef4e1b0aedd736d0fc1.fitBounds(\n", - " [[31.223606110000105, 34.22902679400016], [32.55156707800006, 35.57545471200007]],\n", + " map_2248a8380b652ec76903b2c6f3af61c1.fitBounds(\n", + " [[31.223606110000105, 34.22902679400016], [31.594427109000094, 34.54627227800012]],\n", " {}\n", " );\n", " \n", " \n", - " function geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_styler(feature) {\n", + " function geo_json_64113cf6cb83d18043258a22db4b0cff_styler(feature) {\n", " switch(feature.id) {\n", - " case "0": \n", - " return {"color": "#a00000", "fillColor": "#a00000", "fillOpacity": 0.5, "weight": 2};\n", - " case "1": case "3": \n", - " return {"color": "#980000", "fillColor": "#980000", "fillOpacity": 0.5, "weight": 2};\n", + " case "1": \n", + " return {"color": "#31668e", "fillColor": "#31668e", "fillOpacity": 0.5, "weight": 2};\n", " case "2": \n", - " return {"color": "#930000", "fillColor": "#930000", "fillOpacity": 0.5, "weight": 2};\n", + " return {"color": "#38598c", "fillColor": "#38598c", "fillOpacity": 0.5, "weight": 2};\n", + " case "3": \n", + " return {"color": "#3d4d8a", "fillColor": "#3d4d8a", "fillOpacity": 0.5, "weight": 2};\n", " case "4": \n", - " return {"color": "#b30000", "fillColor": "#b30000", "fillOpacity": 0.5, "weight": 2};\n", - " case "5": \n", - " return {"color": "#e5e5ff", "fillColor": "#e5e5ff", "fillOpacity": 0.5, "weight": 2};\n", - " case "6": \n", - " return {"color": "#c9c9ff", "fillColor": "#c9c9ff", "fillOpacity": 0.5, "weight": 2};\n", - " case "8": \n", - " return {"color": "#d1d1ff", "fillColor": "#d1d1ff", "fillOpacity": 0.5, "weight": 2};\n", - " case "9": case "15": \n", - " return {"color": "#f1f1ff", "fillColor": "#f1f1ff", "fillOpacity": 0.5, "weight": 2};\n", - " case "10": \n", - " return {"color": "#cdcdff", "fillColor": "#cdcdff", "fillOpacity": 0.5, "weight": 2};\n", - " case "11": \n", - " return {"color": "#c5c5ff", "fillColor": "#c5c5ff", "fillOpacity": 0.5, "weight": 2};\n", - " case "12": \n", - " return {"color": "#f9f9ff", "fillColor": "#f9f9ff", "fillOpacity": 0.5, "weight": 2};\n", - " case "14": \n", - " return {"color": "#ddddff", "fillColor": "#ddddff", "fillOpacity": 0.5, "weight": 2};\n", + " return {"color": "#34608d", "fillColor": "#34608d", "fillOpacity": 0.5, "weight": 2};\n", " default:\n", - " return {"color": "#fffdfd", "fillColor": "#fffdfd", "fillOpacity": 0.5, "weight": 2};\n", + " return {"color": "#443983", "fillColor": "#443983", "fillOpacity": 0.5, "weight": 2};\n", " }\n", " }\n", - " function geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_highlighter(feature) {\n", + " function geo_json_64113cf6cb83d18043258a22db4b0cff_highlighter(feature) {\n", " switch(feature.id) {\n", " default:\n", " return {"fillOpacity": 0.75};\n", " }\n", " }\n", - " function geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_pointToLayer(feature, latlng) {\n", + " function geo_json_64113cf6cb83d18043258a22db4b0cff_pointToLayer(feature, latlng) {\n", " var opts = {"bubblingMouseEvents": true, "color": "#3388ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3388ff", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 2, "stroke": true, "weight": 3};\n", " \n", - " let style = geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_styler(feature)\n", + " let style = geo_json_64113cf6cb83d18043258a22db4b0cff_styler(feature)\n", " Object.assign(opts, style)\n", " \n", " return new L.CircleMarker(latlng, opts)\n", " }\n", "\n", - " function geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_onEachFeature(feature, layer) {\n", + " function geo_json_64113cf6cb83d18043258a22db4b0cff_onEachFeature(feature, layer) {\n", " layer.on({\n", " mouseout: function(e) {\n", " if(typeof e.target.setStyle === "function"){\n", - " geo_json_cf6e82337a77a65b97120bdfe7ac0ecd.resetStyle(e.target);\n", + " geo_json_64113cf6cb83d18043258a22db4b0cff.resetStyle(e.target);\n", " }\n", " },\n", " mouseover: function(e) {\n", " if(typeof e.target.setStyle === "function"){\n", - " const highlightStyle = geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_highlighter(e.target.feature)\n", + " const highlightStyle = geo_json_64113cf6cb83d18043258a22db4b0cff_highlighter(e.target.feature)\n", " e.target.setStyle(highlightStyle);\n", " }\n", " },\n", " });\n", " };\n", - " var geo_json_cf6e82337a77a65b97120bdfe7ac0ecd = L.geoJson(null, {\n", - " onEachFeature: geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_onEachFeature,\n", + " var geo_json_64113cf6cb83d18043258a22db4b0cff = L.geoJson(null, {\n", + " onEachFeature: geo_json_64113cf6cb83d18043258a22db4b0cff_onEachFeature,\n", " \n", - " style: geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_styler,\n", - " pointToLayer: geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_pointToLayer\n", + " style: geo_json_64113cf6cb83d18043258a22db4b0cff_styler,\n", + " pointToLayer: geo_json_64113cf6cb83d18043258a22db4b0cff_pointToLayer\n", " });\n", "\n", - " function geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_add (data) {\n", - " geo_json_cf6e82337a77a65b97120bdfe7ac0ecd\n", + " function geo_json_64113cf6cb83d18043258a22db4b0cff_add (data) {\n", + " geo_json_64113cf6cb83d18043258a22db4b0cff\n", " .addData(data)\n", - " .addTo(map_d7f5043427f8fef4e1b0aedd736d0fc1);\n", + " .addTo(map_2248a8380b652ec76903b2c6f3af61c1);\n", " }\n", - " geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_add({"bbox": [34.22902679400016, 31.223606110000105, 35.57545471200007, 32.55156707800006], "features": [{"bbox": [34.30597305300017, 31.372600555000133, 34.415882110000155, 31.462638856000126], "geometry": {"coordinates": [[[34.415882110000155, 31.42720031800019], [34.40790176400009, 31.420608522000123], [34.37456893900003, 31.38384056100017], [34.370647431000066, 31.372600555000133], [34.364295960000106, 31.373163224000052], [34.36157989600008, 31.37386703400017], [34.35890960800015, 31.375257492000117], [34.356483459, 31.377065659000095], [34.35039901700003, 31.38258552500008], [34.34825134300013, 31.38376236000005], [34.34557342600016, 31.38476562500017], [34.33489227300004, 31.38765144300004], [34.32719421400003, 31.39049911600017], [34.31578445500003, 31.39388465999997], [34.311809541000116, 31.395610810000164], [34.309684754000045, 31.396917344000087], [34.30693054200009, 31.399486542000034], [34.30597305300017, 31.400417328000117], [34.30597305300017, 31.400695800999983], [34.306526184000006, 31.400695800999983], [34.306526184000006, 31.400972367000122], [34.30680465700004, 31.400972367000122], [34.30680465700004, 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31.40013885500008], [34.305694580000136, 31.400417328000117], [34.30597305300017, 31.400417328000117], [34.30693054200009, 31.399486542000034], [34.309684754000045, 31.396917344000087], [34.311809541000116, 31.395610810000164], [34.31578445500003, 31.39388465999997], [34.32719421400003, 31.39049911600017], [34.33489227300004, 31.38765144300004], [34.34557342600016, 31.38476562500017], [34.34825134300013, 31.38376236000005], [34.35039901700003, 31.38258552500008], [34.356483459, 31.377065659000095], [34.35890960800015, 31.375257492000117], [34.36157989600008, 31.37386703400017], [34.364295960000106, 31.373163224000052], [34.370647431000066, 31.372600555000133]]], "type": "Polygon"}, "id": "3", "properties": {"CC_2": "NA", "COUNTRY": "Palestine", "ENGTYPE_2": "Governorate", "GID_0": "PSE", "GID_1": "PSE.1_1", "GID_2": "PSE.1.4_1", "HASC_2": "PS.GZ.KY", "NAME_1": "Gaza", "NAME_2": "Khan Yunis", "NL_NAME_1": "NA", "NL_NAME_2": "NA", "TYPE_2": "Governorate", "VARNAME_2": "NA", "__folium_color": "#3d4d8a", "level_0": "ntl_mean", "percent change in radiance average (%)": -76.88237525945152}, "type": "Feature"}, {"bbox": [34.22902679400016, 31.223606110000105, 34.32059097300015, 31.33576202400002], "geometry": {"coordinates": [[[34.268009186000086, 31.223606110000105], [34.262340545000086, 31.239746094000054], [34.23046493400017, 31.330465317999995], [34.22902679400016, 31.33430671800005], [34.22930526700003, 31.33430671800005], [34.22930526700003, 31.334583283000143], [34.229862213000104, 31.334583283000143], [34.229862213000104, 31.33486175600018], [34.23014068600014, 31.33486175600018], [34.23014068600014, 31.335138322000148], [34.23041534400011, 31.335138322000148], [34.23041534400011, 31.335416795000185], [34.230693816999974, 31.335416795000185], [34.230693816999974, 31.335695267999995], [34.23125076300005, 31.33576202400002], [34.235092163000104, 31.334268570999996], [34.238101959999995, 31.333089828000084], [34.240829467000026, 31.331464767], 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"NA", "TYPE_2": "Governorate", "VARNAME_2": "NA", "__folium_color": "#34608d", "level_0": "ntl_mean", "percent change in radiance average (%)": -69.73100167467227}, "type": "Feature"}], "type": "FeatureCollection"});\n", "\n", " \n", " \n", - " geo_json_cf6e82337a77a65b97120bdfe7ac0ecd.bindTooltip(\n", + " geo_json_64113cf6cb83d18043258a22db4b0cff.bindTooltip(\n", " function(layer){\n", " let div = L.DomUtil.create('div');\n", " \n", " let handleObject = feature=>typeof(feature)=='object' ? JSON.stringify(feature) : feature;\n", - " let fields = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "luminosity"];\n", - " let aliases = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "luminosity"];\n", + " let fields = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "percent change in radiance average (%)"];\n", + " let aliases = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "percent change in radiance average (%)"];\n", " let table = '<table>' +\n", " String(\n", " fields.map(\n", @@ -1731,42 +2352,42 @@ " ,{"className": "foliumtooltip", "sticky": true});\n", " \n", " \n", - " var color_map_24de3e2b7a0bf663f2dd8c5087388836 = {};\n", + " var color_map_0e733c92e9ca47a749161fb6104db76f = {};\n", "\n", " \n", - " color_map_24de3e2b7a0bf663f2dd8c5087388836.color = d3.scale.threshold()\n", - " .domain([-100.0, -99.59919839679358, -99.19839679358718, -98.79759519038076, -98.39679358717434, -97.99599198396794, -97.59519038076152, -97.1943887775551, -96.7935871743487, -96.39278557114228, -95.99198396793587, -95.59118236472946, -95.19038076152304, -94.78957915831663, -94.38877755511022, -93.9879759519038, -93.58717434869739, -93.18637274549098, -92.78557114228457, -92.38476953907815, -91.98396793587175, -91.58316633266533, -91.18236472945893, -90.78156312625251, -90.38076152304609, -89.97995991983967, -89.57915831663327, -89.17835671342685, -88.77755511022045, -88.37675350701403, -87.97595190380761, -87.5751503006012, -87.17434869739479, -86.77354709418837, -86.37274549098197, -85.97194388777555, -85.57114228456913, -85.17034068136273, -84.76953907815631, 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color_map_24de3e2b7a0bf663f2dd8c5087388836.legend.addTo(map_d7f5043427f8fef4e1b0aedd736d0fc1);\n", + " color_map_0e733c92e9ca47a749161fb6104db76f.legend = L.control({position: 'topright'});\n", + " color_map_0e733c92e9ca47a749161fb6104db76f.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", + " color_map_0e733c92e9ca47a749161fb6104db76f.legend.addTo(map_2248a8380b652ec76903b2c6f3af61c1);\n", "\n", - " color_map_24de3e2b7a0bf663f2dd8c5087388836.xAxis = d3.svg.axis()\n", - " .scale(color_map_24de3e2b7a0bf663f2dd8c5087388836.x)\n", + " color_map_0e733c92e9ca47a749161fb6104db76f.xAxis = d3.svg.axis()\n", + " .scale(color_map_0e733c92e9ca47a749161fb6104db76f.x)\n", " .orient("top")\n", " .tickSize(1)\n", - " .tickValues([-100.0, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', -79.6078431372549, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 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color_map_0e733c92e9ca47a749161fb6104db76f.g.selectAll("rect")\n", + " .data(color_map_0e733c92e9ca47a749161fb6104db76f.color.range().map(function(d, i) {\n", " return {\n", - " x0: i ? color_map_24de3e2b7a0bf663f2dd8c5087388836.x(color_map_24de3e2b7a0bf663f2dd8c5087388836.color.domain()[i - 1]) : color_map_24de3e2b7a0bf663f2dd8c5087388836.x.range()[0],\n", - " x1: i < color_map_24de3e2b7a0bf663f2dd8c5087388836.color.domain().length ? color_map_24de3e2b7a0bf663f2dd8c5087388836.x(color_map_24de3e2b7a0bf663f2dd8c5087388836.color.domain()[i]) : color_map_24de3e2b7a0bf663f2dd8c5087388836.x.range()[1],\n", + " x0: i ? color_map_0e733c92e9ca47a749161fb6104db76f.x(color_map_0e733c92e9ca47a749161fb6104db76f.color.domain()[i - 1]) : color_map_0e733c92e9ca47a749161fb6104db76f.x.range()[0],\n", + " x1: i < color_map_0e733c92e9ca47a749161fb6104db76f.color.domain().length ? color_map_0e733c92e9ca47a749161fb6104db76f.x(color_map_0e733c92e9ca47a749161fb6104db76f.color.domain()[i]) : color_map_0e733c92e9ca47a749161fb6104db76f.x.range()[1],\n", " z: d\n", " };\n", " }))\n", @@ -1776,24 +2397,33 @@ " .attr("width", function(d) { return d.x1 - d.x0; })\n", " .style("fill", function(d) { return d.z; });\n", "\n", - " color_map_24de3e2b7a0bf663f2dd8c5087388836.g.call(color_map_24de3e2b7a0bf663f2dd8c5087388836.xAxis).append("text")\n", + " color_map_0e733c92e9ca47a749161fb6104db76f.g.call(color_map_0e733c92e9ca47a749161fb6104db76f.xAxis).append("text")\n", " .attr("class", "caption")\n", " .attr("y", 21)\n", - " .text("luminosity");\n", + " .text("percent change in radiance average (%)");\n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "PSE.merge(data.iloc[-1].to_frame(\"luminosity\").reset_index(), on=\"NAME_2\").explore(column=\"luminosity\", cmap=\"seismic_r\", vmin=-100, vmax=100)" + "m = PSE.merge(\n", + " data.iloc[-1].to_frame(\"percent change in radiance average (%)\").reset_index(),\n", + " on=\"NAME_2\",\n", + ").explore(\n", + " column=\"percent change in radiance average (%)\", cmap=\"viridis\", vmin=-100, vmax=0\n", + ")\n", + "\n", + "title_html = f\"\"\"

Week of {data.iloc[-1].name.strftime(\"%F\")}

\"\"\"\n", + "m.get_root().html.add_child(folium.Element(title_html))\n", + "m" ] }, { @@ -1807,7 +2437,7 @@ "tags": [] }, "source": [ - "## Limitations \n", + "# Limitations \n", "\n", "Using nighttime lights to estimate macroeconomic indicators during conflict may be a valuable approach, but it comes with several assumptions and limitations. Here's a list of some of the key assumptions and limitations:\n", "\n", @@ -1844,7 +2474,7 @@ "id": "7922d168-87ab-4fe3-af83-feabd14a3a81", "metadata": {}, "source": [ - "## References\n", + "# References\n", "\n", "{cite:empty}`ROMAN2018113`\n", "\n", @@ -1853,6 +2483,14 @@ ":style: plain\n", "```" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51c53613-27ac-4221-9ebe-cd70f8696bcf", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {