diff --git a/_sources/notebooks/conflict/conflict-and-ntl.ipynb b/_sources/notebooks/conflict/conflict-and-ntl.ipynb
new file mode 100644
index 0000000..949e150
--- /dev/null
+++ b/_sources/notebooks/conflict/conflict-and-ntl.ipynb
@@ -0,0 +1,1809 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "tags": [
+ "remove-cell"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import geopandas as gpd\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 245,
+ "metadata": {
+ "tags": [
+ "remove-cell"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from bokeh.plotting import figure, show\n",
+ "from bokeh.models import LinearAxis, Range1d, Tabs, Panel, TabPanel, ColumnDataSource, HoverTool\n",
+ "\n",
+ "def get_multi_tab_line_plot(df1, df2, loop_list, category_column, conflict_measure, ntl_measure):\n",
+ "\n",
+ " tabs = []\n",
+ "\n",
+ " for ts in loop_list:\n",
+ " # # Create a new Bokeh figure\n",
+ " p = figure(title=f\"Comparing Nighttime Light Trends and {conflict_measure.capitalize()}\", x_axis_label='Month', width=800, height=400, x_axis_type='datetime')\n",
+ "\n",
+ " p.y_range.start = df2[df2[category_column]==ts][conflict_measure].min() \n",
+ " p.y_range.end = df2[df2[category_column]==ts][conflict_measure].max()\n",
+ "\n",
+ " p.extra_y_ranges = {\"y2\": Range1d(start=df1[df1[category_column]==ts][ntl_measure].min(), end=df1[df1[category_column]==ts][ntl_measure].max())}\n",
+ " # Add the second line plot\n",
+ " ntl_source = ColumnDataSource(df1[df1[category_column] == ts])\n",
+ " ntl_line = p.vbar('date', top=ntl_measure,source=ntl_source, legend_label=ntl_measure, width=2000*2000*750, color=\"blue\", alpha=0.7, y_range_name='y2')\n",
+ "\n",
+ "\n",
+ " # Add the first line plot\n",
+ " conflict_source = ColumnDataSource(df2[df2[category_column] == ts])\n",
+ " conflict_line = p.line('date', conflict_measure,source=conflict_source, legend_label=f\"{conflict_measure.capitalize()}\", line_width=2, color=\"red\", alpha=0.7)\n",
+ "\n",
+ " \n",
+ " # Customize legend\n",
+ " p.legend.location = \"top_left\"\n",
+ " p.legend.click_policy = \"hide\" # Optional: Allow clicking to hide/show lines\n",
+ "\n",
+ " p.add_layout(LinearAxis(y_range_name=\"y2\", axis_label='Luminosity'), 'right')\n",
+ "\n",
+ " # Add hover tool for conflict data\n",
+ " hover_conflict = HoverTool(\n",
+ " renderers=[conflict_line], # Reference the renderer directly\n",
+ " tooltips=[\n",
+ " ('Date', '@date{%F}'),\n",
+ " (f'{conflict_measure.capitalize()}', f'@{conflict_measure}'),\n",
+ " ],\n",
+ " formatters={\n",
+ " '@date': 'datetime', # Format the date\n",
+ " },\n",
+ " mode='vline'\n",
+ " )\n",
+ " \n",
+ " # Add hover tool for NTL data\n",
+ " hover_ntl = HoverTool(\n",
+ " renderers=[ntl_line], # Reference the renderer directly\n",
+ " tooltips=[\n",
+ " ('Date', '@date{%F}'),\n",
+ " (ntl_measure, f'@{ntl_measure}'),\n",
+ " ],\n",
+ " formatters={\n",
+ " '@date': 'datetime', # Format the date\n",
+ " },\n",
+ " mode='vline'\n",
+ " )\n",
+ " \n",
+ " p.add_tools(hover_conflict, hover_ntl)\n",
+ "\n",
+ " tab = TabPanel(child=p, title=ts)\n",
+ " tabs.append(tab)\n",
+ "\n",
+ " return tabs\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 276,
+ "metadata": {
+ "tags": [
+ "remove-cell"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from bokeh.plotting import figure, show\n",
+ "from bokeh.models import LinearAxis, Range1d, Tabs, Panel, TabPanel, ColumnDataSource, HoverTool\n",
+ "\n",
+ "def get_multi_tab_bar_plot(df1, loop_list, category_column, ntl_measure):\n",
+ "\n",
+ " tabs = []\n",
+ "\n",
+ " for ts in loop_list:\n",
+ " # # Create a new Bokeh figure\n",
+ " p = figure(title=f\"Nighttime Light Trends\", x_axis_label='Month', width=800, height=400, x_axis_type='datetime')\n",
+ "\n",
+ " ntl_source = ColumnDataSource(df1[df1[category_column] == ts])\n",
+ " p.y_range.start = df1[df1[category_column]==ts][ntl_measure].min() \n",
+ " p.y_range.end = df1[df1[category_column]==ts][ntl_measure].max()\n",
+ " # Add the second line plot\n",
+ " \n",
+ " ntl_line = p.vbar(x='date', top=ntl_measure,source=ntl_source, legend_label=ntl_measure,width=2000*2000*750, color=\"blue\", alpha=0.7)\n",
+ "\n",
+ " # Customize legend\n",
+ " p.legend.location = \"top_left\"\n",
+ " p.legend.click_policy = \"hide\" # Optional: Allow clicking to hide/show lines\n",
+ " \n",
+ " # Add hover tool for NTL data\n",
+ " hover_ntl = HoverTool(\n",
+ " renderers=[ntl_line], # Reference the renderer directly\n",
+ " tooltips=[\n",
+ " ('Date', '@date{%F}'),\n",
+ " (ntl_measure, f'@{ntl_measure}'),\n",
+ " ],\n",
+ " formatters={\n",
+ " '@date': 'datetime', # Format the date\n",
+ " },\n",
+ " mode='vline'\n",
+ " )\n",
+ " \n",
+ " p.add_tools(hover_ntl, hover_ntl)\n",
+ "\n",
+ " tab = TabPanel(child=p, title=ts)\n",
+ " tabs.append(tab)\n",
+ "\n",
+ " return tabs\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Conflict and Nighttime Lights\n",
+ "\n",
+ "This notebook analyses how Nighttime Lights changed as the conflict in the country changed. Conflict is measured using ACLED data and a conflict index is calculated as geometric mean between Number of fatalities and Number of events. This conflict index is then compred to Nighttime lights (measured as lumiinosity) wihout gas flaring. The Nighttime Lights data is retrieved from [BlackMarble](https://datapartnership.org/myanmar-economic-monitor/notebooks/nighttime-lights/analysis-2023/README.html). \n",
+ "\n",
+ "## Insights\n",
+ "\n",
+ "The following questions quide the data analysis - \n",
+ "\n",
+ "- **Is there a direct correlation between Conflict Index and Nighttime Lights?**\n",
+ "- **How did NTL change in regions that saw high increase in conflict compared to 6 months prior?**\n",
+ "- **How is NTL different in EAO controlled areas and other regions?**\n",
+ "\n",
+ "For this, we used the control map of Myanmar as of March 2024 released by the [Special Advisory Council](https://specialadvisorycouncil.org/wp-content/uploads/2024/05/SAC-M-Effective-Control-in-Myanmar-2024-Update-ENGLISH.pdf). The contestation and control is divided into 8 regions starting from 'stable junta control' to 'complete resistance control'. We expect to see that the regions that have moved into 'complete resistance control' have also experienced high increase in conflict. \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Nighttime Lights in EAO Controlled Areas Vs Junta Controlled Areas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 271,
+ "metadata": {
+ "tags": [
+ "remove-cell"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "ntl_adm3_monthly = pd.read_csv('../../data/ntl/ntl_mmr_adm3_monthly.csv')\n",
+ "conflict_monthly = pd.read_stata('../../data/conflict/ACLED_01_Jan_2021_21_Oct_2024_township_monthly_spatial.dta')\n",
+ "conflict_6_monthly = pd.read_stata('../../data/conflict/ACLED_Apr_2022_Sep_2024_township_spatial_period_change.dta')\n",
+ "mmr_adm3 = gpd.read_file('../../data/boundaries/mmr_polbnda_adm3_250k_mimu.shp')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 272,
+ "metadata": {
+ "tags": [
+ "remove-cell"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "ntl_adm3_monthly = mmr_adm3[['ST', 'DT', 'TS', 'geometry']].merge(ntl_adm3_monthly, on = ['ST', 'DT', 'TS'])\n",
+ "ntl_adm3_monthly['date'] = pd.to_datetime(ntl_adm3_monthly['date'])\n",
+ "conflict_monthly.rename(columns={'month':'date'}, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 273,
+ "metadata": {
+ "tags": [
+ "remove-cell"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "control_categories = pd.read_csv('../../data/conflict/control_category_map.csv')\n",
+ "control_categories.rename(columns={'Township': 'TS'}, inplace=True)\n",
+ "eao_controlled = list(control_categories[control_categories['code_no']==8.0]['TS'].unique())\n",
+ "junta_controlled = list(control_categories[control_categories['code_no']==1.0]['TS'].unique())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 274,
+ "metadata": {
+ "tags": [
+ "remove-cell"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "ntl_adm3_monthly = ntl_adm3_monthly.merge(control_categories, on = 'TS')\n",
+ "ntl_adm3_monthly['code_no'] = ntl_adm3_monthly['code_no'].astype(str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 284,
+ "metadata": {
+ "tags": [
+ "remove-cell"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "conflict_monthly = conflict_monthly.merge(control_categories, on = 'TS')\n",
+ "conflict_monthly['code_no'] = conflict_monthly['code_no'].astype(str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 287,
+ "metadata": {
+ "tags": [
+ "remove-cell"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "control_code_dict = {'1.0': \"Stable junta control\",\n",
+ "'2.0': \"Junta dependent on local proxy militias for control\",\n",
+ "'3.0':\"Junta forces under regular attack from resistance forces; administration functions remain weak\",\n",
+ "'4.0':\"Resistance controls growing territory but still cannot consolidate fuller control\",\n",
+ "'5.0':\"Limited junta movement, dependent on ceasefires\",\n",
+ "'6.0':\"Junta control receding; resistance defending increasing territories & asserting local administration\",\n",
+ "'7.0':\"Strong resistance control & local administration—90%+ of township\",\n",
+ "'8.0':\"Full resistance control & local administration—whole township\"}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- 1:Stable junta control\n",
+ "- 2:Junta dependent on local proxy militias for control\n",
+ "- 3:Junta forces under regular attack from resistance forces; administration functions remain weak\n",
+ "- 4:Resistance controls growing territory but still cannot consolidate fuller control\n",
+ "- 5:Limited junta movement, dependent on ceasefires\n",
+ "- 6:Junta control receding; resistance defending increasing territories & asserting local administration\n",
+ "- 7:Strong resistance control & local administration—90%+ of township\n",
+ "- 8:Full resistance control & local administration—whole township"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 288,
+ "metadata": {
+ "tags": [
+ "remove-input"
+ ]
+ },
+ "outputs": [
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+ "application/vnd.bokehjs_exec.v0+json": ""
+ },
+ "metadata": {
+ "application/vnd.bokehjs_exec.v0+json": {
+ "id": "p82670"
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Import necessary libraries\n",
+ "output_notebook()\n",
+ "from bokeh.models import LinearAxis, Range1d, Tabs, Panel, TabPanel, HoverTool, ColumnDataSource\n",
+ "\n",
+ "df1 = ntl_adm3_monthly[['TS','code_no', 'date', 'ntl_sum', 'ntl_nogf_5km_sum']].groupby(['date','code_no'])[['ntl_sum', 'ntl_nogf_5km_sum']].mean().reset_index()\n",
+ "df1=df1[df1['date']>'2021-01-1']\n",
+ "ntl_measure = 'ntl_nogf_5km_sum'\n",
+ "\n",
+ "\n",
+ "tabs = get_multi_tab_bar_plot(df1,loop_list=list(control_code_dict.keys()), category_column='code_no', ntl_measure=ntl_measure)\n",
+ "\n",
+ "# Create the Tabs layout\n",
+ "tabs_layout = Tabs(tabs=tabs)\n",
+ "\n",
+ "# Show the tabs\n",
+ "show(tabs_layout)\n",
+ "\n",
+ " # # Show the plot\n",
+ " # show(p)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**There is a huge peak in the currently EAO controlled regions back in April 2023 where there is massive peak in nighttime lights. The same peak is not visible in the junta controlled regions.** There is a lot more consistent light in regions where junta forces are under regular attack (category 3)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Nighttime Lights in EAO Controlled Townships"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 280,
+ "metadata": {
+ "tags": [
+ "remove-input"
+ ]
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " \n",
+ "
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+ " \n",
+ " Loading BokehJS ...\n",
+ "
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\\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"
+ ]
+ },
+ "metadata": {},
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const render_items = [{\"docid\":\"400a086c-589d-4686-bf2b-ff9294d9f5d8\",\"roots\":{\"p81514\":\"eb7b4ad6-19da-4504-91cb-1f473fb6da05\"},\"root_ids\":[\"p81514\"]}];\n void 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": "p81514"
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Import necessary libraries\n",
+ "output_notebook()\n",
+ "from bokeh.models import LinearAxis, Range1d, Tabs, Panel, TabPanel, HoverTool, ColumnDataSource\n",
+ "\n",
+ "df1 = ntl_adm3_monthly[['TS','code_no', 'date', 'ntl_sum', 'ntl_nogf_5km_sum']].groupby(['date','TS'])[['ntl_sum', 'ntl_nogf_5km_sum']].mean().reset_index()\n",
+ "df1=df1[df1['date']>'2021-01-1']\n",
+ "ntl_measure = 'ntl_nogf_5km_sum'\n",
+ "\n",
+ "\n",
+ "tabs = get_multi_tab_bar_plot(df1,loop_list=junta_controlled, category_column='TS', ntl_measure=ntl_measure)\n",
+ "\n",
+ "# Create the Tabs layout\n",
+ "tabs_layout = Tabs(tabs=tabs)\n",
+ "\n",
+ "# Show the tabs\n",
+ "show(tabs_layout)\n",
+ "\n",
+ " # # Show the plot\n",
+ " # show(p)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Comparing Trends in Conflict with Nighttime Lights\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 278,
+ "metadata": {
+ "tags": [
+ "remove-cell"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "very_severe_conflict = list(conflict_monthly[(conflict_monthly['conflict_category']=='Very severe conflict')&(conflict_monthly['date']>='2024-03-01')]['TS'].unique())\n",
+ "severe_conflict = list(conflict_monthly[(conflict_monthly['conflict_category']=='Severe conflict')&(conflict_monthly['date']>='2024-03-01')]['TS'].unique())\n",
+ "no_conflict = list(conflict_monthly[(conflict_monthly['conflict_category']=='No conflict')&(conflict_monthly['date']>='2024-03-01')]['TS'].unique())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Conflict and Nighttime Lights in EAO Controlled Areas Vs Junta Controlled Areas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 290,
+ "metadata": {
+ "tags": [
+ "remove-input"
+ ]
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " \n",
+ "
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+ "metadata": {},
+ "output_type": "display_data"
+ },
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+ "data": {
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\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p83350\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p83351\"},{\"type\":\"object\",\"name\":\"HelpTool\",\"id\":\"p83352\"},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p83385\",\"attributes\":{\"renderers\":[{\"id\":\"p83377\"}],\"tooltips\":[[\"Date\",\"@date{%F}\"],[\"Conflict_index\",\"@conflict_index\"]],\"formatters\":{\"type\":\"map\",\"entries\":[[\"@date\",\"datetime\"]]},\"mode\":\"vline\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p83386\",\"attributes\":{\"renderers\":[{\"id\":\"p83363\"}],\"tooltips\":[[\"Date\",\"@date{%F}\"],[\"ntl_nogf_5km_sum\",\"@ntl_nogf_5km_sum\"]],\"formatters\":{\"type\":\"map\",\"entries\":[[\"@date\",\"datetime\"]]},\"mode\":\"vline\"}}]}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p83337\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p83338\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p83339\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p83340\"}}}],\"right\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p83381\",\"attributes\":{\"y_range_name\":\"y2\",\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p83382\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p83383\"},\"axis_label\":\"Luminosity\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p83384\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"DatetimeAxis\",\"id\":\"p83320\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"DatetimeTicker\",\"id\":\"p83321\",\"attributes\":{\"num_minor_ticks\":5,\"tickers\":[{\"type\":\"object\",\"name\":\"AdaptiveTicker\",\"id\":\"p83322\",\"attributes\":{\"num_minor_ticks\":0,\"mantissas\":[1,2,5],\"max_interval\":500.0}},{\"type\":\"object\",\"name\":\"AdaptiveTicker\",\"id\":\"p83323\",\"attributes\":{\"num_minor_ticks\":0,\"base\":60,\"mantissas\":[1,2,5,10,15,20,30],\"min_interval\":1000.0,\"max_interval\":1800000.0}},{\"type\":\"object\",\"name\":\"AdaptiveTicker\",\"id\":\"p83324\",\"attributes\":{\"num_minor_ticks\":0,\"base\":24,\"mantissas\":[1,2,4,6,8,12],\"min_interval\":3600000.0,\"max_interval\":43200000.0}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p83325\",\"attributes\":{\"days\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p83326\",\"attributes\":{\"days\":[1,4,7,10,13,16,19,22,25,28]}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p83327\",\"attributes\":{\"days\":[1,8,15,22]}},{\"type\":\"object\",\"name\":\"DaysTicker\",\"id\":\"p83328\",\"attributes\":{\"days\":[1,15]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p83329\",\"attributes\":{\"months\":[0,1,2,3,4,5,6,7,8,9,10,11]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p83330\",\"attributes\":{\"months\":[0,2,4,6,8,10]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p83331\",\"attributes\":{\"months\":[0,4,8]}},{\"type\":\"object\",\"name\":\"MonthsTicker\",\"id\":\"p83332\",\"attributes\":{\"months\":[0,6]}},{\"type\":\"object\",\"name\":\"YearsTicker\",\"id\":\"p83333\"}]}},\"formatter\":{\"type\":\"object\",\"name\":\"DatetimeTickFormatter\",\"id\":\"p83334\"},\"axis_label\":\"Month\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p83335\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p83336\",\"attributes\":{\"axis\":{\"id\":\"p83320\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p83341\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p83337\"}}},{\"type\":\"object\",\"name\":\"Legend\",\"id\":\"p83366\",\"attributes\":{\"location\":\"top_left\",\"click_policy\":\"hide\",\"items\":[{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p83367\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"ntl_nogf_5km_sum\"},\"renderers\":[{\"id\":\"p83363\"}]}},{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p83380\",\"attributes\":{\"label\":{\"type\":\"value\",\"value\":\"Conflict_index\"},\"renderers\":[{\"id\":\"p83377\"}]}}]}}]}}}}]}}]}};\n const render_items = [{\"docid\":\"e64bfbed-85fa-4956-8bea-7db1962f0005\",\"roots\":{\"p83388\":\"d911da3a-8ebc-4a9c-b3ec-22b019d2f015\"},\"root_ids\":[\"p83388\"]}];\n void 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": "p83388"
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Import necessary libraries\n",
+ "output_notebook()\n",
+ "from bokeh.models import LinearAxis, Range1d, Tabs, Panel, TabPanel, HoverTool, ColumnDataSource\n",
+ "\n",
+ "df1 = ntl_adm3_monthly[['TS','code_no', 'date', 'ntl_sum', 'ntl_nogf_5km_sum']].groupby(['date','code_no'])[['ntl_sum', 'ntl_nogf_5km_sum']].mean().reset_index()\n",
+ "df1=df1[df1['date']>'2021-01-1']\n",
+ "ntl_measure = 'ntl_nogf_5km_sum'\n",
+ "\n",
+ "df2 = conflict_monthly.groupby(['date', 'code_no'], observed=False)[['conflict_index', 'fatalities', 'events']].mean().reset_index()\n",
+ "df2.dropna(subset='conflict_index', inplace=True)\n",
+ "conflict_measure = 'conflict_index'\n",
+ "\n",
+ "tabs = get_multi_tab_line_plot(df1,df2,loop_list=list(control_code_dict.keys()),category_column='code_no', conflict_measure=conflict_measure, ntl_measure=ntl_measure)\n",
+ "\n",
+ "# Create the Tabs layout\n",
+ "tabs_layout = Tabs(tabs=tabs)\n",
+ "\n",
+ "# Show the tabs\n",
+ "show(tabs_layout)\n",
+ "\n",
+ " # # Show the plot\n",
+ " # show(p)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 283,
+ "metadata": {
+ "tags": [
+ "remove-input"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "# # Import necessary libraries\n",
+ "# from bokeh.io import output_notebook\n",
+ "# output_notebook()\n",
+ "\n",
+ "\n",
+ "\n",
+ "# # If you are running this in a Jupyter notebook, uncomment the next line\n",
+ "# # output_notebook()\n",
+ "\n",
+ "# df1 = ntl_adm3_monthly.groupby(['date'])[['ntl_sum', 'ntl_gf_5km_sum',\n",
+ "# 'ntl_nogf_5km_sum', 'ntl_gf_10km_sum', 'ntl_nogf_10km_sum']].mean().reset_index()\n",
+ "# df1=df1[df1['date']>'2021-01-1']\n",
+ "# df2 = conflict_monthly.groupby(['date'])[['conflict_index', 'fatalities', 'events']].mean().reset_index()\n",
+ "# ntl_measure = 'ntl_nogf_5km_sum'\n",
+ "\n",
+ "# tabs = []\n",
+ "\n",
+ "# for id, conflict_measure in enumerate(['conflict_index', 'events', 'fatalities']):\n",
+ "\n",
+ "# # Create a new Bokeh figure\n",
+ "# p = figure(title=f\"Nighttime Light Trends and {conflict_measure.capitalize()} at a National Level\", x_axis_label='Month', width=800, height=400, x_axis_type='datetime')\n",
+ "\n",
+ "# p.y_range.start = 0 \n",
+ "# p.y_range.end = df2[conflict_measure].max()\n",
+ "\n",
+ "# # Add the first line plot\n",
+ "# p.line(df2['date'], df2[conflict_measure], legend_label=f\"{conflict_measure.capitalize()}\", line_width=2, color=\"red\", alpha=0.7)\n",
+ "\n",
+ "# p.extra_y_ranges = {\"y2\": Range1d(start=0, end=df1[ntl_measure].max())}\n",
+ "# # Add the second line plot\n",
+ "# p.vbar(df1['date'], top=df1[ntl_measure], legend_label=ntl_measure, width=2000*2000*750, color=\"blue\", alpha=0.7, y_range_name='y2')\n",
+ "\n",
+ "# # Customize legend\n",
+ "# p.legend.location = \"top_left\"\n",
+ "# p.legend.click_policy = \"hide\" # Optional: Allow clicking to hide/show lines\n",
+ "\n",
+ "# p.add_layout(LinearAxis(y_range_name=\"y2\", axis_label='Luminosity'), 'right')\n",
+ "\n",
+ "# tab = TabPanel(child=p, title=conflict_measure.capitalize())\n",
+ "# tabs.append(tab)\n",
+ "\n",
+ "# # Create the Tabs layout\n",
+ "# tabs_layout = Tabs(tabs=tabs)\n",
+ "\n",
+ "# # Show the tabs\n",
+ "# show(tabs_layout)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**There is no obvious correlation between NTL and Conflict Index, Events or Fatalities at a national level**. In fact, in some periods it has a positive correlation where the NTL goes up along with conflict. To inspect this further, we broke it down at a regional level. The entire country saw an increase in NTL in February 2023. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Conflict and NTL in Very severely and severely conflicted regions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 282,
+ "metadata": {
+ "tags": [
+ "remove-input"
+ ]
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "
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+ " \n",
+ " Loading BokehJS ...\n",
+ "
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+ "application/vnd.bokehjs_exec.v0+json": ""
+ },
+ "metadata": {
+ "application/vnd.bokehjs_exec.v0+json": {
+ "id": "p82055"
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Import necessary libraries\n",
+ "output_notebook()\n",
+ "from bokeh.models import LinearAxis, Range1d, Tabs, Panel, TabPanel, HoverTool, ColumnDataSource\n",
+ "\n",
+ "df1 = ntl_adm3_monthly.groupby(['date', 'TS'])[['ntl_sum', 'ntl_gf_5km_sum',\n",
+ " 'ntl_nogf_5km_sum', 'ntl_gf_10km_sum', 'ntl_nogf_10km_sum']].mean().reset_index()\n",
+ "df1=df1[df1['date']>'2021-01-1']\n",
+ "df2 = conflict_monthly.groupby(['date', 'TS', 'conflict_category'], observed=False)[['conflict_index', 'fatalities', 'events']].mean().reset_index()\n",
+ "df2.dropna(subset='conflict_index', inplace=True)\n",
+ "conflict_measure = 'conflict_index'\n",
+ "ntl_measure = 'ntl_nogf_5km_sum'\n",
+ "\n",
+ "tabs = get_multi_tab_line_plot(df1,df2,loop_list=very_severe_conflict,category_column='TS', conflict_measure=conflict_measure, ntl_measure=ntl_measure)\n",
+ "\n",
+ "# Create the Tabs layout\n",
+ "tabs_layout = Tabs(tabs=tabs)\n",
+ "\n",
+ "# Show the tabs\n",
+ "show(tabs_layout)\n",
+ "\n",
+ " # # Show the plot\n",
+ " # show(p)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**In Maungdaw, there is a corresponding increase of NTL with conflict index in August 2022 and in April 2024. Similarly, there is a decrease in NTL with conflict in October 2022.** The opposite movement is seen in May 2022 when the NTL goes up and the conflict came down. \n",
+ "\n",
+ "**In Lashio, October 2023 saw a corresponding increase in NTL and Conflict.** However, early in 2024 the same level of NTL also has a lower level of conflict. \n",
+ "\n",
+ "No convincing trends can be identified to understand the relationship between these variables yet. A simple correlation plot between conflict index and NTL for severely conflicted locations and very severely conflicted location also reveals that no trend can be seen. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "tags": [
+ "remove-input"
+ ]
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " \n",
+ "
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+ " \n",
+ " Loading BokehJS ...\n",
+ "
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+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ {
+ "data": {
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+ "application/vnd.bokehjs_exec.v0+json": ""
+ },
+ "metadata": {
+ "application/vnd.bokehjs_exec.v0+json": {
+ "id": "p3985"
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Import necessary libraries\n",
+ "output_notebook()\n",
+ "from bokeh.models import LinearAxis, Range1d, Tabs, Panel, TabPanel, HoverTool, ColumnDataSource\n",
+ "from bokeh.plotting import figure, show\n",
+ "import numpy as np\n",
+ "from scipy import stats # For linear regression\n",
+ "\n",
+ "# Data preparation\n",
+ "df1 = ntl_adm3_monthly.groupby(['date', 'TS'])[['ntl_sum', 'ntl_gf_5km_sum',\n",
+ " 'ntl_nogf_5km_sum', 'ntl_gf_10km_sum', 'ntl_nogf_10km_sum']].mean().reset_index()\n",
+ "df1 = df1[df1['date'] > '2021-01-01']\n",
+ "\n",
+ "df2 = conflict_monthly.groupby(['date', 'TS', 'conflict_category'], observed=False)[['conflict_index', 'fatalities', 'events']].mean().reset_index()\n",
+ "df2.dropna(subset='conflict_index', inplace=True)\n",
+ "\n",
+ "conflict_measure = 'conflict_index'\n",
+ "ntl_measure = 'ntl_nogf_5km_sum'\n",
+ "\n",
+ "tabs = []\n",
+ "\n",
+ "# Loop through severe conflicts and create a scatter plot with a best fit line for each\n",
+ "for ts in very_severe_conflict:\n",
+ " # Merge df1 and df2 on 'date' and 'TS' to align conflict and NTL data for the scatter plot\n",
+ " merged_df = pd.merge(df1[df1['TS'] == ts], df2[df2['TS'] == ts], on=['date', 'TS'])\n",
+ "\n",
+ " # Create a new Bokeh figure\n",
+ " p = figure(title=f\"Conflict vs Nighttime Light (TS={ts})\", \n",
+ " x_axis_label=f\"{conflict_measure.capitalize()}\", \n",
+ " y_axis_label=f\"{ntl_measure}\", \n",
+ " width=800, height=400)\n",
+ "\n",
+ " # Scatter plot with conflict on x-axis and NTL on y-axis using `scatter()`\n",
+ " scatter_source = ColumnDataSource(merged_df)\n",
+ " scatter_plot = p.scatter(conflict_measure, ntl_measure, source=scatter_source, \n",
+ " size=8, color=\"blue\", alpha=0.7)\n",
+ "\n",
+ " # Add hover tool to show both conflict and NTL values\n",
+ " hover_scatter = HoverTool(\n",
+ " renderers=[scatter_plot],\n",
+ " tooltips=[\n",
+ " ('Date', '@date{%F}'),\n",
+ " (f'{conflict_measure.capitalize()}', f'@{conflict_measure}'),\n",
+ " (ntl_measure, f'@{ntl_measure}')\n",
+ " ],\n",
+ " formatters={'@date': 'datetime'}, # Format the date\n",
+ " mode='mouse'\n",
+ " )\n",
+ " \n",
+ " p.add_tools(hover_scatter)\n",
+ "\n",
+ " # Calculate the best fit line using scipy's linear regression\n",
+ " x = merged_df[conflict_measure].values\n",
+ " y = merged_df[ntl_measure].values\n",
+ " slope, intercept, r_value, p_value, std_err = stats.linregress(x, y)\n",
+ "\n",
+ " # Best fit line points\n",
+ " best_fit_y = intercept + slope * x\n",
+ "\n",
+ " # Plot the best fit line\n",
+ " p.line(x, best_fit_y, line_width=2, color=\"red\", legend_label=\"Best Fit Line\")\n",
+ "\n",
+ " # Create a tab for each conflict\n",
+ " tab = TabPanel(child=p, title=str(ts))\n",
+ " tabs.append(tab)\n",
+ "\n",
+ "# Create the Tabs layout\n",
+ "tabs_layout = Tabs(tabs=tabs)\n",
+ "\n",
+ "# Show the tabs\n",
+ "show(tabs_layout)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "tags": [
+ "remove-input"
+ ]
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " \n",
+ "
\n",
+ " \n",
+ " Loading BokehJS ...\n",
+ "
\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\\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"
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\\n\"+\n \"
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\\n\"+\n \"
\\n\"+\n \"
re-rerun `output_notebook()` to attempt to load from CDN again, or
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const render_items = [{\"docid\":\"ca523866-e774-4001-8902-a67f907afd9d\",\"roots\":{\"p51769\":\"e1ec8a1a-8af4-4207-9648-c3af6f374c28\"},\"root_ids\":[\"p51769\"]}];\n void 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": "p51769"
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Import necessary libraries\n",
+ "output_notebook()\n",
+ "from bokeh.models import LinearAxis, Range1d, Tabs, Panel, TabPanel, HoverTool, ColumnDataSource\n",
+ "\n",
+ "\n",
+ "df1=df1[df1['date']>'2021-01-1']\n",
+ "conflict_measure = 'conflict_index_diff_pm'\n",
+ "ntl_measure = 'ntl_nogf_5km_diff_pm'\n",
+ "\n",
+ "tabs = get_multi_tab_line_plot(df1,df2,very_severe_conflict, 'TS', conflict_measure, ntl_measure)\n",
+ "\n",
+ "# Create the Tabs layout\n",
+ "tabs_layout = Tabs(tabs=tabs)\n",
+ "\n",
+ "# Show the tabs\n",
+ "show(tabs_layout)\n",
+ "\n",
+ " # # Show the plot\n",
+ " # show(p)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 186,
+ "metadata": {
+ "tags": [
+ "remove-input"
+ ]
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " \n",
+ "
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+ " \n",
+ " Loading BokehJS ...\n",
+ "
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\\n\"+\n \"
\\n\"+\n \"
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const render_items = [{\"docid\":\"0cf1099f-cbbd-45fc-b886-8a1bcd4b1915\",\"roots\":{\"p52624\":\"ba35f78d-b14b-4cc9-ae2c-21a29207b5f2\"},\"root_ids\":[\"p52624\"]}];\n void 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": "p52624"
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Import necessary libraries\n",
+ "output_notebook()\n",
+ "from bokeh.models import LinearAxis, Range1d, Tabs, Panel, TabPanel, HoverTool, ColumnDataSource\n",
+ "\n",
+ "\n",
+ "df1=df1[df1['date']>'2021-01-1']\n",
+ "conflict_measure = 'conflict_index_diff_pm'\n",
+ "ntl_measure = 'ntl_nogf_5km_diff_pm'\n",
+ "\n",
+ "tabs = get_multi_tab_line_plot(df1,df2,no_conflict[0:10], 'TS', conflict_measure, ntl_measure)\n",
+ "\n",
+ "# Create the Tabs layout\n",
+ "tabs_layout = Tabs(tabs=tabs)\n",
+ "\n",
+ "# Show the tabs\n",
+ "show(tabs_layout)\n",
+ "\n",
+ " # # Show the plot\n",
+ " # show(p)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 118,
+ "metadata": {
+ "tags": [
+ "remove-input"
+ ]
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " \n",
+ "
\n",
+ " \n",
+ " Loading BokehJS ...\n",
+ "
\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": "'use strict';\n(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\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n function drop(id) {\n const view = Bokeh.index.get_by_id(id)\n if (view != null) {\n view.model.document.clear()\n Bokeh.index.delete(view)\n }\n }\n\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n\n // Clean up Bokeh references\n if (id != null) {\n drop(id)\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim()\n drop(id)\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\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"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\\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"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": "'use strict';\n(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\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n function drop(id) {\n const view = Bokeh.index.get_by_id(id)\n if (view != null) {\n view.model.document.clear()\n Bokeh.index.delete(view)\n }\n }\n\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n\n // Clean up Bokeh references\n if (id != null) {\n drop(id)\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim()\n drop(id)\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\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
This notebook analyses how Nighttime Lights changed as the conflict in the country changed. Conflict is measured using ACLED data and a conflict index is calculated as geometric mean between Number of fatalities and Number of events. This conflict index is then compred to Nighttime lights (measured as lumiinosity) wihout gas flaring. The Nighttime Lights data is retrieved from BlackMarble.
Is there a direct correlation between Conflict Index and Nighttime Lights?
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How did NTL change in regions that saw high increase in conflict compared to 6 months prior?
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How is NTL different in EAO controlled areas and other regions?
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For this, we used the control map of Myanmar as of March 2024 released by the Special Advisory Council. The contestation and control is divided into 8 regions starting from ‘stable junta control’ to ‘complete resistance control’. We expect to see that the regions that have moved into ‘complete resistance control’ have also experienced high increase in conflict.
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Nighttime Lights in EAO Controlled Areas Vs Junta Controlled Areas#
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1:Stable junta control
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2:Junta dependent on local proxy militias for control
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3:Junta forces under regular attack from resistance forces; administration functions remain weak
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4:Resistance controls growing territory but still cannot consolidate fuller control
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5:Limited junta movement, dependent on ceasefires
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6:Junta control receding; resistance defending increasing territories & asserting local administration
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7:Strong resistance control & local administration—90%+ of township
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8:Full resistance control & local administration—whole township
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There is a huge peak in the currently EAO controlled regions back in April 2023 where there is massive peak in nighttime lights. The same peak is not visible in the junta controlled regions. There is a lot more consistent light in regions where junta forces are under regular attack (category 3).
Comparing Trends in Conflict with Nighttime Lights#
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Conflict and Nighttime Lights in EAO Controlled Areas Vs Junta Controlled Areas#
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There is no obvious correlation between NTL and Conflict Index, Events or Fatalities at a national level. In fact, in some periods it has a positive correlation where the NTL goes up along with conflict. To inspect this further, we broke it down at a regional level. The entire country saw an increase in NTL in February 2023.
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Conflict and NTL in Very severely and severely conflicted regions#
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In Maungdaw, there is a corresponding increase of NTL with conflict index in August 2022 and in April 2024. Similarly, there is a decrease in NTL with conflict in October 2022. The opposite movement is seen in May 2022 when the NTL goes up and the conflict came down.
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In Lashio, October 2023 saw a corresponding increase in NTL and Conflict. However, early in 2024 the same level of NTL also has a lower level of conflict.
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No convincing trends can be identified to understand the relationship between these variables yet. A simple correlation plot between conflict index and NTL for severely conflicted locations and very severely conflicted location also reveals that no trend can be seen.
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Comparing difference in conflict with difference in NTL for a 6-month-period#
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Comparing NTL and Conflict Increase from Baseline period of October 2020-March 2021#
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diff --git a/notebooks/mobility/activity.html b/notebooks/mobility/activity.html
index 7f29135..ce95b1b 100644
--- a/notebooks/mobility/activity.html
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