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app.py

@@ -1,98 +1,557 @@
-from shiny import App, ui, render, reactive
-from shinywidgets import render_widget
-from ipyleaflet import Map, TileLayer, GeoJSON, LayersControl
+# app.py
+from shiny import App, ui, reactive, render
 import pandas as pd
 import numpy as np
-import plotly.express as px
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
 import matplotlib.pyplot as plt
-from ipywidgets import Output
 import rasterio
+from rasterio.plot import show
+import geopandas as gpd
+from ipyleaflet import Map, TileLayer, basemaps, ColorMap, RasterLayer, LegendControl, GeoJSON
+from shinywidgets import output_widget, register_widget
+import plotnine as p9
+# from palettable.colorbrewer.diverging import Spectral_10 
+# from palettable.colorbrewer.sequential import Blues_9, OrRd_9, PuBuGn_9, Reds_9
+import os
+import base64
+import tempfile
 import json
+from datetime import datetime
+from fetch_data import fetch_data
+from residuals import get_residual_plot
 
-# ---- 1. Load Data ----
-# Example file paths (replace with actual paths or S3 calls)
-wealth_tif_path = "data/wealth_map.tif"
-improvement_csv_path = "data/poverty_improvement_by_state.csv"
+# ------------------------------
+# 1. Data & Config
+# ------------------------------
 
-# Read CSV
-improvement_data = pd.read_csv(improvement_csv_path)
+# Define time periods corresponding to each band in the GeoTIFF
+time_periods = ["1990–1992", "1993–1995", "1996–1998", "1999–2001", "2002–2004",
+               "2005–2007", "2008–2010", "2011–2013", "2014–2016", "2017–2019"]
 
-# Load raster stack
-wealth_stack = rasterio.open(wealth_tif_path)
+# Load GeoTIFF data (multi-band)
+# Note: In a real application, you'd need to adjust this path
+wealth_stack = rasterio.open("wealth_map.tif")
 
-# Time periods corresponding to bands
-time_periods = [
-    "1990–1992", "1993–1995", "1996–1998", "1999–2001", "2002–2004",
-    "2005–2007", "2008–2010", "2011–2013", "2014–2016", "2017–2019"
-]
+with open('data/no_somaliland.geojson') as a: 
+    country_json = json.load(a)
+  
+IWI_df = pd.read_csv('data/mean_IWI_by_country.csv')
 
-# ---- 2. UI ----
+residual_data = pd.read_csv('data/residual_by_country.csv')
+
+
+
+
+# Function to clean up out-of-range values and get values
+def get_clean_values(src, band_idx=1):
+    band_data = src.read(band_idx)
+    # Replace out-of-range values with NaN
+    band_data[(band_data <= 0) | (band_data > 1)] = np.nan
+    return band_data
+
+# Get all values across all bands for quantiles
+all_vals = []
+for i in range(1, wealth_stack.count + 1):
+    vals = get_clean_values(wealth_stack, i).flatten()
+    all_vals.extend(vals[~np.isnan(vals)])
+
+all_vals = np.array(all_vals)
+q_breaks_legend = np.quantile(all_vals, np.linspace(0, 1, 6))
+q_breaks = np.quantile(all_vals, np.linspace(0, 1, 11))
+
+# Get raster bounds for proper positioning on the map
+bounds = [[wealth_stack.bounds.bottom, wealth_stack.bounds.left], 
+          [wealth_stack.bounds.top, wealth_stack.bounds.right]]
+
+# Load improvement data (change in IWI by state/province)
+# In real app, adjust path
+improvement_data = pd.read_csv("data/poverty_improvement_by_state.csv")
+
+# Pre-calculate the mean IWI for each band (for the "Trends Over Time" chart)
+band_means = []
+for i in range(1, wealth_stack.count + 1):
+    vals = get_clean_values(wealth_stack, i).flatten()
+    band_means.append(np.nanmean(vals))
+
+# ------------------------------
+# 2. UI
+# ------------------------------
+
+# Custom CSS for OCR A Std font and other styling
+css = """
+@import url('https://fonts.cdnfonts.com/css/ocr-a-std');
+
+body {
+  font-family: 'OCR A Std', monospace !important;
+}
+
+.slider-animate-button {
+  background-color: #ffffff !important;
+  color: #000000 !important;
+  border: 2px solid #000000 !important;
+  border-radius: 5px !important;
+  padding: 5px 10px !important;
+  top: 10px !important;
+}
+
+.value-box {
+  margin-bottom: 15px;
+  padding: 15px;
+  border-radius: 5px;
+  color: white;
+}
+
+.green-box {
+  background-color: #00a65a;
+}
+
+.blue-box {
+  background-color: #0073b7;
+}
+
+.red-box {
+  background-color: #dd4b39;
+}
+
+.share-button {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  gap: 8px; 
+  padding: 5px 10px;
+  font-size: 16px;
+  font-weight: normal;
+  color: #000;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 6px;
+  cursor: pointer;
+  box-shadow: 0 1.5px 0 #000;
+}
+
+.title-text {
+  font-family: 'OCR A Std', monospace; 
+  font-size: 18px;
+}
+
+.subtitle-text {
+  font-family: 'OCR A Std', monospace; 
+  font-size: 14px;
+}
+#improvement_table .shiny-data-grid {
+        width: 100% !important;
+    }
+.nav-link {
+    color: white !important;
+}
+"""
+
+# Share button HTML
+share_button_html = """
+<button id="share-button" class="share-button">
+  <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" 
+       stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
+    <circle cx="18" cy="5" r="3"></circle>
+    <circle cx="6" cy="12" r="3"></circle>
+    <circle cx="18" cy="19" r="3"></circle>
+    <line x1="8.59" y1="13.51" x2="15.42" y2="17.49"></line>
+    <line x1="15.41" y1="6.51" x2="8.59" y2="10.49"></line>
+  </svg>
+  <strong>Share</strong>
+</button>
+
+<script>
+(function() {
+  const shareBtn = document.getElementById('share-button');
+  // Reusable helper function to show a small "Copied!" message
+  function showCopyNotification() {
+    const notification = document.createElement('div');
+    notification.innerText = 'Copied to clipboard';
+    notification.style.position = 'fixed';
+    notification.style.bottom = '20px';
+    notification.style.right = '20px';
+    notification.style.backgroundColor = 'rgba(0, 0, 0, 0.8)';
+    notification.style.color = '#fff';
+    notification.style.padding = '8px 12px';
+    notification.style.borderRadius = '4px';
+    notification.style.zIndex = '9999';
+    document.body.appendChild(notification);
+    setTimeout(() => { notification.remove(); }, 2000);
+  }
+  shareBtn.addEventListener('click', function() {
+    const currentURL = window.location.href;
+    const pageTitle  = document.title || 'Check this out!';
+    // If browser supports Web Share API
+    if (navigator.share) {
+      navigator.share({
+        title: pageTitle,
+        text: '',
+        url: currentURL
+      })
+      .catch((error) => {
+        console.log('Sharing failed', error);
+      });
+    } else {
+      // Fallback: Copy URL
+      if (navigator.clipboard && navigator.clipboard.writeText) {
+        navigator.clipboard.writeText(currentURL).then(() => {
+          showCopyNotification();
+        }, (err) => {
+          console.error('Could not copy text: ', err);
+        });
+      } else {
+        // Double fallback for older browsers
+        const textArea = document.createElement('textarea');
+        textArea.value = currentURL;
+        document.body.appendChild(textArea);
+        textArea.select();
+        try {
+          document.execCommand('copy');
+          showCopyNotification();
+        } catch (err) {
+          alert('Please copy this link:\\n' + currentURL);
+        }
+        document.body.removeChild(textArea);
+      }
+    }
+  });
+})();
+</script>
+"""
+
+# Create the app UI with dashboard layout
 app_ui = ui.page_fluid(
-    ui.h2("Africa Wealth Map Dashboard (Python Version)"),
-    ui.layout_sidebar(
-        ui.panel_sidebar(
-            ui.input_slider("time_index", "Select Time Period (Years):", 1, 10, 1),
-            ui.input_select("color_palette", "Select Color Palette:", {
-                "viridis": "Viridis", "plasma": "Plasma", "magma": "Magma",
-                "inferno": "Inferno", "Spectral": "Spectral (Brewer)"
-            }),
-            ui.input_slider("opacity", "Map Opacity:", 0.2, 1.0, 0.8, step=0.1)
+    ui.head_content(
+        ui.tags.style(css)
+    ),
+    ui.page_navbar(
+        ui.nav_panel("Wealth Map",
+            ui.layout_sidebar(
+                ui.sidebar(
+                    ui.h4("Map Controls"),
+                    ui.input_slider(
+                        "time_index", 
+                        "Select Time Period (Years):", 
+                        min=1, 
+                        max=len(time_periods), 
+                        value=1, 
+                        step=1,
+                        animate=True
+                    ),
+                    ui.strong("Currently Selected: "),
+                    ui.output_text("current_year_range", inline=True),
+                    ui.input_select(
+                        "color_palette", 
+                        "Select Color Palette:",
+                        {
+                            "blue": "blue",
+                            "red": "red",
+                            "orange": "orange",
+                            "purple": "purple",
+                            "Spectral": "Spectral (Brewer)"
+                        },
+                        selected="red"
+                    ),
+                    ui.input_slider(
+                        "opacity", 
+                        "Map Opacity:", 
+                        min=0.2, 
+                        max=1, 
+                        value=0.8, 
+                        step=0.1
+                    ),
+                    ui.HTML(share_button_html)
+                ),
+                ui.layout_column_wrap(
+                    ui.value_box(
+                        "Highest IWI", 
+                        ui.output_text("highest_iwi"),
+                        showcase=ui.tags.i(class_="fa fa-arrow-up"),
+                        theme="success"
+                    ),
+                    ui.value_box(
+                        "Lowest IWI", 
+                        ui.output_text("lowest_iwi"),
+                        showcase=ui.tags.i(class_="fa fa-arrow-down"),
+                        theme="danger"
+                    ),
+                    ui.value_box(
+                        "Average IWI", 
+                        ui.output_text("avg_iwi"),
+                        showcase=ui.tags.i(class_="fa fa-balance-scale"),
+                        theme="primary"
+                    ),
+                    width=1/3
+                ),
+                ui.layout_column_wrap(
+                    ui.card(
+                        ui.card_header(ui.h3("Wealth Map of Africa", class_="title-text")),
+                        output_widget("map"),
+                        ui.p("Click anywhere on the map to view the time-series of IWI for that specific location (shown below).")
+                    ),
+                    ui.card(
+                      ui.card_header(ui.h3("Time Series at Clicked Location", class_="subtitle-text")),
+                      ui.output_plot("clicked_ts_plot"),
+                      ui.p("Click on the map to see the full IWI time-series (1990–2019) for that location.")
+                  )
+                ),
+                
+                ui.card(
+                        ui.card_header(ui.h3("Ground Truth vs. Prediction Residual Distribution (Selected Country)", class_="subtitle-text")),
+                        ui.output_plot("iwi_residuals"),
+                        ui.p("This chart shows the distribution of residuals between ground truth and predicted IWI values based on the selected country."),
+                        ui.strong("Note: wealth estimates for areas without human settlements have been excluded from the analysis."),
+                        ui.HTML("<a href='https://doi.org/10.24963/ijcai.2023/684' target='_blank'>[Paper PDF]</a>")
+                    ),
+            )
         ),
-        ui.panel_main(
-            ui.output_text("current_year_range"),
-            ui.output_plot("iwi_histogram"),
-            ui.output_plot("trend_plot"),
-            ui.output_plot("clicked_ts_plot")
-        )
-    )
+        ui.nav_panel("Improvement Data",
+            ui.layout_columns(
+            ui.card(
+                ui.card_header(ui.h3("Poverty Improvement by State", class_="title-text")),
+                ui.p("This table shows the estimated improvement in mean IWI between 1990–1992 and 2017–2019 for each province in Africa. "
+                   "The 'Improvement' column indicates the change in IWI over this period. You can sort or filter the table, "
+                   "and use the download button to export the data."),
+                ui.download_button("download_data", "Download CSV", icon="download"),
+                ui.card(ui.output_data_frame("improvement_table")),
+                
+            )
+            )
+        ),
+        ui.nav_panel("Trends Over Time",
+            ui.card(
+                ui.card_header(ui.h3("Average Wealth Index Across Africa Over Time", class_="title-text")),
+                ui.p("This chart aggregates the mean IWI across all of Africa in each of the ten time periods. "
+                   "It provides a high-level view of how wealth (as measured by IWI) has changed over time."),
+                ui.output_plot("trend_plot")
+            )
+        ),
+        title=ui.HTML(
+            "<span style='font-weight: 600; font-size: 16px;'>"
+            "<a href='http://aidevlab.org' target='_blank' "
+            "style='font-family: \"OCR A Std\", monospace; color: white; text-decoration: underline;'>"
+            "aidevlab.org</a></span>"
+        ),
+        id="tabs",
+        bg="#337ab7"
+    ),
 )
 
-# ---- 3. Server ----
+# ------------------------------
+# 3. Server logic
+# ------------------------------
 def server(input, output, session):
+    # Initialize the map widget
+    m = Map(center=(0, 20), zoom=3)
+    geo_json = GeoJSON(
+        data=country_json,
+        style={
+            'opacity': 1, 'dashArray': '9', 'fillOpacity': 0.1, 'weight': 1
+        },
+        hover_style={
+            'color': 'white', 'dashArray': '0', 'fillOpacity': 0.5
+        }
+    )
+    m.add_layer(geo_json)
+
+    
 
-    selected_band = reactive.Calc(lambda: input.time_index())
+    # Register the map widget with Shiny
+    map_widget = register_widget("map", m)
+    
+    # Store clicked point values
+    clicked_point_vals = reactive.Value(None)
+    selected_country = reactive.Value(None)
 
+    admin_layer = reactive.Value(None)
+    selected_admin = reactive.Value(None)
+    
+    # Get the currently selected raster layer
+    @reactive.Calc
+    def selected_raster():
+        band_idx = input.time_index()
+        return get_clean_values(wealth_stack, band_idx)
+    
+    # Display selected time period
     @output
     @render.text
     def current_year_range():
-        return time_periods[input.time_index() - 1]
+        return time_periods[input.time_index() - 1]  # Adjust for 0-based indexing
+    
+    # Function to get color palette based on user selection
+    # @reactive.Calc
+    # def get_palette():
+    #     palette_name = input.color_palette()
+    #     if palette_name == "blue":
+    #         return Blues_9.hex_colors 
+    #     elif palette_name == "orange":
+    #         return OrRd_9.hex_colors
+    #     elif palette_name == "red":
+    #         return Reds_9.hex_colors
+    #     elif palette_name == "purple":
+    #         return PuBuGn_9.hex_colors
+    #     else:  # Spectral
+    #         return Spectral_10.hex_colors
+    
+    # Create a RasterLayer for the map
+    # @reactive.effect
+    # @reactive.event(input.time_index, input.color_palette, input.opacity)
+    # def _():
+    #     # Remove existing raster layers
+    #     for layer in m.layers:
+    #         if isinstance(layer, RasterLayer):
+    #             m.remove_layer(layer)
+        
+    #     # Get current raster data
+    #     raster_data = selected_raster()
+        
+    #     # Create a temporary GeoTIFF file
+    #     with tempfile.NamedTemporaryFile(suffix='.tif', delete=False) as tmp:
+    #         temp_path = tmp.name
+            
+    #         # Create a new GeoTIFF with the selected band
+    #         with rasterio.open(
+    #             temp_path,
+    #             'w',
+    #             driver='GTiff',
+    #             height=raster_data.shape[0],
+    #             width=raster_data.shape[1],
+    #             count=1,
+    #             dtype=raster_data.dtype,
+    #             crs=wealth_stack.crs,
+    #             transform=wealth_stack.transform,
+    #         ) as dst:
+    #             dst.write(raster_data, 1)
+        
+    #     # Create a ColorMap for the raster
+    #     colormap = ColorMap(
+    #         vmin=q_breaks[0],
+    #         vmax=q_breaks[-1]
+    #         # palette=get_palette()
+    #     )
+        
+    #     # Add the raster layer to the map
+    #     raster_layer = RasterLayer(
+    #         url=temp_path,
+    #         bounds=bounds,
+    #         colormap=colormap,
+    #         opacity=input.opacity()
+    #     )
+        
+    #     m.add_layer(raster_layer)
+        
+    #     # Add legend
+    #     for ctrl in m.controls:
+    #         if isinstance(ctrl, LegendControl):
+    #             m.remove_control(ctrl)
+                
+    #     legend = LegendControl({"IWI": colormap}, position="bottomright")
+    #     m.add_control(legend)
+    
+    # Handle map clicks
+    @reactive.effect
+    def _():
+        # Set up click event handler
+        def handle_map_click(event = None, feature = None, **kwargs):
+            coords = feature['geometry']['coordinates'][0] #extract feature coordinates 
+            latitudes = [coords[x][1] for x in range(len(coords))] 
+            longitudes = [coords[y][0] for y in range(len(coords))]
+            country_name= feature['properties']['sovereignt'] #find country name 
+            country_abbrev= feature['properties']['sov_a3'] #find country abbreviation
 
-    @output
-    @render.plot
-    def iwi_histogram():
-        band_index = input.time_index() - 1
-        band_data = wealth_stack.read(band_index + 1).flatten()
-        band_data = band_data[(band_data > 0) & (band_data <= 1)]
-        plt.figure(figsize=(6, 4))
-        plt.hist(band_data, bins=20, color="skyblue", edgecolor="white")
-        plt.title("IWI Histogram")
-        plt.xlabel("IWI")
-        plt.ylabel("Frequency")
-        return plt.gcf()
+            selected_country.set(country_name) #set the country name 
 
+            centroid = (np.mean(latitudes),np.mean(longitudes)) #lock view position to the country's centroid 
+            m.center = centroid
+            m.zoom = 5
+        
+        # Register click handler
+        geo_json.on_click(handle_map_click)
+    
+    # Display value boxes
+    @output
+    @render.text
+    def highest_iwi():
+        raster_data = selected_raster()
+        return f"{np.nanmax(raster_data):.3f}"
+    
+    @output
+    @render.text
+    def lowest_iwi():
+        raster_data = selected_raster()
+        return f"{np.nanmin(raster_data):.3f}"
+    
+    @output
+    @render.text
+    def avg_iwi():
+        raster_data = selected_raster()
+        return f"{np.nanmean(raster_data):.3f}"
+    
+    # Generate trend plot for mean IWI across Africa
     @output
     @render.plot
     def trend_plot():
-        mean_iwi = []
-        for i in range(1, 11):
-            band_data = wealth_stack.read(i).flatten()
-            band_data = band_data[(band_data > 0) & (band_data <= 1)]
-            mean_iwi.append(np.mean(band_data))
-
-        fig = go.Figure()
-        fig.add_trace(go.Scatter(x=time_periods, y=mean_iwi, mode='lines+markers'))
-        fig.update_layout(title="Average IWI Over Time", xaxis_title="Period", yaxis_title="Mean IWI")
+        fig, ax = plt.subplots(figsize=(12, 8))
+        ax.plot(range(len(time_periods)), band_means, marker='o', color="darkorange", linewidth=2, markersize=6)
+        ax.set_xticks(range(len(time_periods)))
+        ax.set_xticklabels(time_periods, rotation=45, ha="right")
+        ax.set_ylabel("Mean IWI")
+        ax.set_ylim(0.1, 0.3)
+        ax.set_title("Average IWI Over Time (Africa)")
+        ax.grid(True, linestyle='--', alpha=0.7)
+        
+        plt.tight_layout()
         return fig
-
+    
+    # Generate histogram plot
+    @output
+    @render.plot
+    def iwi_residuals():
+        country_name = selected_country.get()
+        fig = get_residual_plot(country_name, residual_data)
+        return fig 
+    
+    # Plot time series at clicked location
     @output
     @render.plot
     def clicked_ts_plot():
-        # Placeholder until integrated with map click logic
-        fig = go.Figure()
-        fig.update_layout(title="Click on map to load time series")
+        country_name = selected_country.get()
+        
+        fig, ax = plt.subplots(figsize=(10, 4))
+        
+        if country_name is None:
+            ax.text(0.5, 0.5, "Click on the map to see the IWI time-series here.",
+                   horizontalalignment='center', verticalalignment='center',
+                   transform=ax.transAxes, fontsize=14)
+        else:
+            ax.plot(IWI_df['Band_Number'], IWI_df[country_name], marker='o', color="darkorange", linewidth=2, markersize=6)
+            ax.set_xticks(range(1,len(IWI_df['Band_Number'])+1))
+            ax.set_xticklabels(time_periods, rotation=45)
+            ax.set_ylabel("IWI (0 to 1)")
+            ax.set_ylim(0, 1)
+            ax.set_title(f"Time Series of IWI in {country_name}")
+            ax.grid(True, linestyle='--', alpha=0.7)
+        
+        plt.tight_layout()
         return fig
+    
+    # Display improvement data table
+    @output
+    @render.data_frame
+    def improvement_table():
+        return render.DataGrid(
+            improvement_data,
+            filters=True,
+            height="800px"
+        )
+    
+    # Download CSV handler
+    @session.download(filename=lambda: f"poverty_improvement_{datetime.now().strftime('%Y-%m-%d')}.csv")
+    def download_data():
+        return improvement_data.to_csv(index=False)
+    
+    
 
-# ---- 4. App ----
-app = App(app_ui, server)
+# ------------------------------
+# 4. Create and run the app
+# ------------------------------
+app = App(app_ui, server)

fetch_data.py

@@ -0,0 +1,23 @@
+import boto3
+import dotenv
+import os 
+import json 
+import pandas as pd
+import keys #py file to store AWS access keys 
+
+def fetch_data(key_path):
+    access_key, secret_key = keys.return_keys()
+    bucket_name = "ai-and-global-development-lab"
+    s3_client = boto3.client('s3', aws_access_key_id=access_key, aws_secret_access_key=secret_key)
+
+    response = s3_client.get_object(Bucket=bucket_name, Key=key_path)
+    if ".geojson" in key_path or  ".json" in key_path:
+        file_content = response['Body'].read().decode('utf-8')
+        json_content = json.loads(file_content)
+        return json_content
+
+    elif ".csv" in key_path:
+        file_content = response['Body']
+        df_content = pd.read_csv(file_content)
+        return df_content
+    

keys.py

@@ -0,0 +1,4 @@
+def return_keys():
+    AWS_ACCESS_KEY = "AKIAWN26KCOURF7SVJN6"
+    AWS_SECRET_ACCESS_KEY = "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"
+    return AWS_ACCESS_KEY, AWS_SECRET_ACCESS_KEY

mean_IWI_by_country.csv

@@ -0,0 +1,11 @@
+,Band_Number,Band_Number,Burundi,Benin,Burkina Faso,Ivory Coast,Democratic Republic of the Congo,Angola,Botswana,Cameroon,Republic of the Congo,Central African Republic,Djibouti,Algeria,Egypt,Gabon,Eritrea,Ghana,Ethiopia,Guinea,Gambia,Guinea Bissau,Morocco,Liberia,Lesotho,Equatorial Guinea,Libya,Madagascar,Kenya,Mozambique,Mauritania,Niger,Rwanda,Western Sahara,Nigeria,Sudan,Malawi,Namibia,Senegal,South Sudan,Mali,Somaliland,Swaziland,Chad,Somalia,Sierra Leone,Togo,Tunisia,Uganda,Tanzania,South Africa,Zambia,Zimbabwe,Band_Number
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+5,6,6,0.15510750854104813,0.1791621603572752,0.180544332242929,0.18995065372947642,0.14608297780116142,0.15288577283073257,0.2174530963234554,0.18371138322664315,0.1868198239615175,0.14620676393356785,0.17403035486737886,0.2636778048520901,0.4645401599061865,0.21604991986995606,0.1647712812848287,0.20254796595790797,0.14357037374253742,0.15285098971269453,0.1925043334819113,0.16228917929488168,0.23100478862306026,0.16278005060496353,0.1667824779386536,0.21282876796351793,0.316242472680778,0.14026866332848661,0.17616876956919103,0.15294158051821213,0.1836515212490786,0.15031814160714815,0.1618641481213903,0.23957519233226776,0.19454539169419605,0.2192205141008414,0.15670192530486185,0.2033620594804516,0.20697273706061564,0.16525641377020744,0.17613290617036784,0.1730959749813425,0.18632847454864532,0.1672610971987172,0.1807569135494983,0.15004218377887385,0.17592207193027287,0.26089484484453623,0.1760213400675057,0.16303444741229597,0.23780704238400677,0.16425568626600312,0.18436713656157222,6
+6,7,7,0.16028250787491188,0.1907362270704556,0.19164785357753625,0.1994043046063316,0.15041300854816497,0.16195695175655941,0.22783480006337956,0.19324491027399157,0.2008908811126905,0.1513704288901621,0.17501220703125,0.2862842581950027,0.49887416602712625,0.23710178164985235,0.16924569864420294,0.217281407703554,0.14856844394827293,0.15930923965332586,0.20774021945965634,0.17414424917792196,0.24313912406244773,0.1644996051522894,0.17184356993615033,0.23987550001878005,0.3443522288881499,0.14296342206923343,0.1840075274419518,0.15887666930262617,0.18874510999843566,0.14861782177074537,0.16842529039342696,0.227783203125,0.20653116251072234,0.23661287641936166,0.162731162885056,0.204814033767804,0.2226238250732422,0.17992252677813914,0.18830449109990124,0.17808589650623835,0.19657352138748369,0.17414636045279908,0.1920832217727577,0.15579702039087495,0.18356950291377147,0.281051690970483,0.18359144857107115,0.16888550158460522,0.2467528439083615,0.17279310361052438,0.1887763142261287,7
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poverty_improvement_by_state.csv

@@ -0,0 +1,856 @@
+"Country","Province","Rank","Improvement"
+"Gambia","Banjul",1,0.31955
+"Mali","Bamako",2,0.31376
+"Botswana","Francistown",3,0.28345
+"Egypt","Kafr ash Shaykh",4,0.27021
+"Algeria","Boumerdès",5,0.26634
+"Egypt","Ash Sharqiyah",6,0.26594
+"Egypt","Bur Sa`id",7,0.26321
+"Egypt","Ad Daqahliyah",8,0.26129
+"Egypt","Luxor",9,0.25765
+"Egypt","Dumyat",10,0.25121
+"Botswana","Gaborone",11,0.24716
+"Central African Republic","Bangui",12,0.24536
+"Egypt","Al Gharbiyah",13,0.24513
+"Egypt","Al Minufiyah",14,0.22851
+"Morocco","Grand Casablanca",15,0.22812
+"Botswana","Lobatse",16,0.22754
+"Algeria","Tizi Ouzou",17,0.22517
+"Libya","Tajura' wa an Nawahi al Arba",18,0.22499
+"Mozambique","Maputo",19,0.22482
+"Algeria","Sétif",20,0.22442
+"Angola","Luanda",21,0.22147
+"Tunisia","Ben Arous (Tunis Sud)",22,0.2203
+"Egypt","Asyut",23,0.21946
+"Algeria","Mila",24,0.21815
+"Algeria","Jijel",25,0.21676
+"Egypt","Qina",26,0.21588
+"Libya","Al Jifarah",27,0.21512
+"Egypt","Al Buhayrah",28,0.21469
+"Algeria","Blida",29,0.21357
+"Algeria","Constantine",30,0.20791
+"Algeria","Tipaza",31,0.20667
+"Algeria","Mostaganem",32,0.20647
+"Tunisia","Manubah",33,0.20299
+"Niger","Niamey",34,0.20255
+"Egypt","Al Qalyubiyah",35,0.2005
+"Tunisia","Monastir",36,0.19901
+"Algeria","Bordj Bou Arréridj",37,0.19734
+"Ethiopia","Addis Ababa",38,0.19314
+"Algeria","Béjaïa",39,0.1923
+"Algeria","Aïn Témouchent",40,0.19053
+"Kenya","Nairobi",41,0.19051
+"Tunisia","Mahdia",42,0.18971
+"Zimbabwe","Harare",43,0.18842
+"Egypt","Suhaj",44,0.18763
+"Algeria","Annaba",45,0.18544
+"Algeria","Mascara",46,0.18378
+"Algeria","Bouira",47,0.18271
+"Algeria","Oran",48,0.18269
+"Algeria","Aïn Defla",49,0.18008
+"Algeria","Chlef",50,0.17978
+"Algeria","El Tarf",51,0.1785
+"Tunisia","Zaghouan",52,0.17821
+"Morocco","Gharb - Chrarda - Béni Hssen",53,0.17795
+"Senegal","Thiès",54,0.17778
+"Algeria","Skikda",55,0.17667
+"Senegal","Dakar",56,0.17611
+"Tunisia","Sousse",57,0.17483
+"Algeria","Alger",58,0.17438
+"Tunisia","Kairouan",59,0.17427
+"Egypt","Al Qahirah",60,0.17394
+"Algeria","Guelma",61,0.17316
+"Algeria","Oum el Bouaghi",62,0.17177
+"Algeria","Batna",63,0.17102
+"Uganda","Kampala",64,0.17047
+"Republic of the Congo","Pointe Noire",65,0.17029
+"Tunisia","Jendouba",66,0.16717
+"Tunisia","Nabeul",67,0.16704
+"South Africa","Gauteng",68,0.16687
+"Egypt","Al Iskandariyah",69,0.16506
+"Burkina Faso","Kadiogo",70,0.16066
+"Tunisia","Béja",71,0.16058
+"Egypt","Al Isma`iliyah",72,0.15984
+"United Republic of Tanzania","Dar-Es-Salaam",73,0.15948
+"Tunisia","Tunis",74,0.15855
+"Zimbabwe","Bulawayo",75,0.15845
+"Botswana","Selebi-Phikwe",76,0.15845
+"Algeria","Relizane",77,0.15655
+"United Republic of Tanzania","Zanzibar West",78,0.15601
+"Senegal","Kaolack",79,0.1557
+"Tunisia","Bizerte",80,0.15402
+"Algeria","Souk Ahras",81,0.15168
+"Morocco","Chaouia - Ouardigha",82,0.14985
+"Senegal","Diourbel",83,0.14867
+"Libya","Az Zawiyah",84,0.14612
+"Guinea Bissau","Bissau",85,0.14429
+"Tunisia","Sfax",86,0.14233
+"Benin","Ouémé",87,0.14005
+"Tunisia","Siliana",88,0.13976
+"Morocco","Doukkala - Abda",89,0.13972
+"Algeria","Médéa",90,0.13814
+"Ivory Coast","Fromager",91,0.13803
+"Gambia","Central River",92,0.13574
+"Morocco","Marrakech - Tensift - Al Haouz",93,0.13534
+"Ivory Coast","Sud-Bandama",94,0.13477
+"Egypt","Al Fayyum",95,0.13474
+"Botswana","Jwaneng",96,0.13446
+"Ivory Coast","Lacs",97,0.1341
+"Gambia","Lower River",98,0.13397
+"Gambia","Upper River",99,0.13338
+"Mauritania","Nouakchott",100,0.13255
+"Gambia","West Coast",101,0.13239
+"Chad","Ville de N'Djamena",102,0.13192
+"Tunisia","Le Kef",103,0.13012
+"Rwanda","Kigali City",104,0.12999
+"Tunisia","Sidi Bou Zid",105,0.12966
+"Algeria","Tissemsilt",106,0.12949
+"Ghana","Greater Accra",107,0.12897
+"Burkina Faso","Oubritenga",108,0.12846
+"Algeria","Tlemcen",109,0.12639
+"Sudan","Gezira",110,0.12632
+"Algeria","Sidi Bel Abbès",111,0.12507
+"Ivory Coast","Lagunes",112,0.12452
+"Botswana","South-East",113,0.12289
+"Nigeria","Lagos",114,0.12281
+"Burundi","Bujumbura Mairie",115,0.12195
+"Burkina Faso","Kourwéogo",116,0.12043
+"Burkina Faso","Boulkiemdé",117,0.11939
+"Ghana","Central",118,0.11906
+"Senegal","Fatick",119,0.11895
+"Benin","Atlantique",120,0.1184
+"Egypt","Bani Suwayf",121,0.11738
+"Swaziland","Hhohho",122,0.11599
+"Libya","Al Marqab",123,0.11548
+"Ivory Coast","Agnéby",124,0.11455
+"Morocco","Tanger - Tétouan",125,0.1144
+"Uganda","Wakiso",126,0.11269
+"Ivory Coast","Haut-Sassandra",127,0.11109
+"Swaziland","Manzini",128,0.11051
+"Ivory Coast","Marahoué",129,0.10905
+"Nigeria","Imo",130,0.10903
+"Ivory Coast","Comoe",131,0.10868
+"Mali","Sikasso",132,0.10761
+"Liberia","Montserrado",133,0.10637
+"Swaziland","Shiselweni",134,0.10624
+"South Africa","Mpumalanga",135,0.10558
+"Ghana","Ashanti",136,0.10554
+"Burkina Faso","Léraba",137,0.10516
+"United Republic of Tanzania","Kaskazini-Pemba",138,0.10438
+"Eritrea","Anseba",139,0.10418
+"Burkina Faso","Passoré",140,0.10293
+"Senegal","Ziguinchor",141,0.10192
+"Algeria","M'Sila",142,0.10051
+"South Africa","Limpopo",143,0.09866
+"South Africa","North West",144,0.09866
+"United Republic of Tanzania","Kaskazini-Unguja",145,0.09855
+"Morocco","Taza - Al Hoceima - Taounate",146,0.09815
+"Burkina Faso","Kénédougou",147,0.09774
+"South Africa","KwaZulu-Natal",148,0.09651
+"Senegal","Sédhiou",149,0.09603
+"Benin","Mono",150,0.09475
+"Sierra Leone","Western",151,0.09447
+"Nigeria","Ekiti",152,0.09424
+"Nigeria","Abia",153,0.09423
+"Ivory Coast","Bas-Sassandra",154,0.09414
+"Libya","An Nuqat al Khams",155,0.09382
+"Tunisia","Médenine",156,0.09314
+"Ivory Coast","N'zi-Comoé",157,0.09263
+"Morocco","Rabat - Salé - Zemmour - Zaer",158,0.09249
+"South Africa","Free State",159,0.09102
+"Tunisia","Gabès",160,0.09005
+"Nigeria","Osun",161,0.08957
+"Gambia","North Bank",162,0.08956
+"Ethiopia","Harari People",163,0.08895
+"Tunisia","Kassérine",164,0.08787
+"Burundi","Bubanza",165,0.0876
+"Morocco","Tadla - Azilal",166,0.08631
+"Ghana","Western",167,0.08617
+"Guinea","Labé",168,0.08613
+"Ivory Coast","Savanes",169,0.08534
+"Burkina Faso","Balé",170,0.08523
+"Guinea Bissau","Biombo",171,0.08512
+"Ghana","Upper East",172,0.08394
+"Lesotho","Berea",173,0.08332
+"Burkina Faso","Mou Houn",174,0.08322
+"Botswana","North-East",175,0.08284
+"United Republic of Tanzania","Zanzibar South and Central",176,0.08265
+"Tunisia","Gafsa",177,0.08257
+"Burkina Faso","Bam",178,0.08232
+"Morocco","Souss - Massa - Draâ",179,0.08226
+"Burkina Faso","Ganzourgou",180,0.08222
+"Nigeria","Anambra",181,0.08209
+"Sudan","Khartoum",182,0.08203
+"Malawi","Blantyre",183,0.08107
+"Nigeria","Sokoto",184,0.08106
+"Malawi","Lilongwe",185,0.081
+"Ivory Coast","Sud-Comoé",186,0.08048
+"Nigeria","Zamfara",187,0.08046
+"Lesotho","Mafeteng",188,0.08033
+"Botswana","Sowa",189,0.0802
+"Guinea Bissau","Cacheu",190,0.07948
+"Nigeria","Kebbi",191,0.07796
+"Togo","Maritime",192,0.07777
+"Burundi","Rutana",193,0.07749
+"Swaziland","Lubombo",194,0.07749
+"Equatorial Guinea","Kié-Ntem",195,0.07741
+"Ghana","Eastern",196,0.07715
+"Malawi","Dedza",197,0.07681
+"Algeria","Khenchela",198,0.07654
+"Burkina Faso","Bazéga",199,0.07622
+"Burkina Faso","Boulgou",200,0.07617
+"Malawi","Chiradzulu",201,0.07532
+"Libya","Benghazi",202,0.07522
+"Malawi","Mulanje",203,0.0748
+"Somalia","Banaadir",204,0.07416
+"Liberia","Margibi",205,0.07409
+"Malawi","Thyolo",206,0.07357
+"Burkina Faso","Yatenga",207,0.0735
+"Burkina Faso","Zoundwéogo",208,0.07336
+"Malawi","Dowa",209,0.07251
+"Nigeria","Akwa Ibom",210,0.07242
+"Uganda","Sheema",211,0.07236
+"Ivory Coast","Worodougou",212,0.07228
+"Ethiopia","Dire Dawa",213,0.07164
+"Algeria","Saïda",214,0.0709
+"Eritrea","Maekel",215,0.0702
+"Burkina Faso","Sourou",216,0.07016
+"Guinea","Conakry",217,0.07007
+"Guinea","Coyah",218,0.06942
+"Burundi","Bujumbura Rural",219,0.06896
+"Malawi","Mchinji",220,0.06845
+"Burkina Faso","Zondoma",221,0.06801
+"Guinea","Lélouma",222,0.06774
+"Nigeria","Ogun",223,0.06762
+"Burundi","Mwaro",224,0.06752
+"Algeria","Tébessa",225,0.06694
+"Malawi","Ntchisi",226,0.06681
+"Burundi","Bururi",227,0.06679
+"Burundi","Karuzi",228,0.06661
+"Senegal","Kaffrine",229,0.06633
+"Liberia","Grand Bassa",230,0.06613
+"Equatorial Guinea","Wele-Nzás",231,0.0661
+"Uganda","Mukono",232,0.06603
+"Burkina Faso","Houet",233,0.06596
+"Burundi","Cibitoke",234,0.06564
+"Uganda","Mayuge",235,0.06536
+"Lesotho","Leribe",236,0.06512
+"Malawi","Ntcheu",237,0.06472
+"Burkina Faso","Sanguié",238,0.06444
+"Burkina Faso","Kouritenga",239,0.06418
+"Uganda","Bushenyi",240,0.06391
+"Ghana","Upper West",241,0.06365
+"Burundi","Makamba",242,0.06293
+"Guinea","Mandiana",243,0.06283
+"Algeria","Tiaret",244,0.06279
+"Uganda","Mpigi",245,0.06259
+"Burundi","Kayanza",246,0.0624
+"Benin","Plateau",247,0.06238
+"Burundi","Ngozi",248,0.06218
+"Ivory Coast","Vallée du Bandama",249,0.06203
+"Burundi","Muramvya",250,0.06167
+"Burkina Faso","Banwa",251,0.06131
+"Rwanda","Southern",252,0.06122
+"Burundi","Muyinga",253,0.0612
+"Ghana","Brong Ahafo",254,0.06116
+"Guinea Bissau","Oio",255,0.0611
+"Nigeria","Gombe",256,0.06094
+"Malawi","Balaka",257,0.06085
+"Guinea","Pita",258,0.06083
+"United Republic of Tanzania","Kusini-Pemba",259,0.06069
+"Egypt","As Suways",260,0.0605
+"South Africa","Eastern Cape",261,0.06048
+"Guinea","Dubréka",262,0.06007
+"Guinea","Nzérékoré",263,0.06002
+"Burundi","Gitega",264,0.05988
+"Namibia","Ohangwena",265,0.05933
+"Malawi","Phalombe",266,0.05891
+"Madagascar","Itasy",267,0.05876
+"Equatorial Guinea","Bioko Norte",268,0.05858
+"Ivory Coast","Dix-Huit Montagnes",269,0.05819
+"Angola","Cabinda",270,0.05815
+"Malawi","Neno",271,0.0578
+"Nigeria","Enugu",272,0.0575
+"Nigeria","Ondo",273,0.05744
+"Burundi","Ruyigi",274,0.0573
+"Nigeria","Kano",275,0.05681
+"Libya","Al Jabal al Akhdar",276,0.05667
+"Uganda","Ntungamo",277,0.0566
+"Algeria","Biskra",278,0.05659
+"Guinea","Forécariah",279,0.05634
+"Burkina Faso","Sanmatenga",280,0.05634
+"Ivory Coast","Denguélé",281,0.05612
+"Burundi","Kirundo",282,0.05584
+"Uganda","Luweero",283,0.05576
+"Malawi","Zomba",284,0.05502
+"Ghana","Volta",285,0.0548
+"Egypt","Al Minya",286,0.05452
+"Rwanda","Eastern",287,0.05445
+"Senegal","Louga",288,0.05424
+"Lesotho","Maseru",289,0.05359
+"Morocco","Meknès - Tafilalet",290,0.05346
+"Burkina Faso","Tuy",291,0.05344
+"Malawi","Salima",292,0.05334
+"Liberia","Bong",293,0.05332
+"Malawi","Mangochi",294,0.05248
+"Uganda","Mitooma",295,0.05245
+"Uganda","Mbarara",296,0.05206
+"Guinea","Siguiri",297,0.05175
+"Zambia","Copperbelt",298,0.0516
+"South Africa","Western Cape",299,0.05143
+"Zimbabwe","Mashonaland East",300,0.05101
+"Guinea Bissau","Tombali",301,0.05091
+"Libya","Al Marj",302,0.05076
+"Malawi","Machinga",303,0.05043
+"Guinea","Fria",304,0.04977
+"Ivory Coast","Zanzan",305,0.04963
+"Burkina Faso","Sissili",306,0.04958
+"Burkina Faso","Koulpélogo",307,0.0494
+"Benin","Kouffo",308,0.04903
+"Mali","Ségou",309,0.04872
+"Morocco","Fès - Boulemane",310,0.0487
+"Mali","Koulikoro",311,0.04866
+"Guinea","Boffa",312,0.04832
+"Guinea","Yomou",313,0.04806
+"Guinea","Macenta",314,0.04774
+"Liberia","Nimba",315,0.04759
+"Guinea","Beyla",316,0.04728
+"Liberia","Bomi",317,0.04709
+"Benin","Borgou",318,0.04708
+"Uganda","Butambala",319,0.04696
+"Malawi","Kasungu",320,0.04687
+"Niger","Dosso",321,0.04668
+"Guinea","Dalaba",322,0.04656
+"Benin","Donga",323,0.04651
+"Uganda","Busia",324,0.04636
+"Uganda","Kabale",325,0.04616
+"Burkina Faso","Nayala",326,0.04612
+"Burkina Faso","Komoé",327,0.046
+"Uganda","Bugiri",328,0.04589
+"Uganda","Iganga",329,0.04587
+"Burkina Faso","Nahouri",330,0.04507
+"Madagascar","Analamanga",331,0.045
+"Guinea Bissau","Quinara",332,0.04458
+"Guinea","Guéckédou",333,0.04457
+"Nigeria","Edo",334,0.04454
+"Ivory Coast","Bafing",335,0.04434
+"Guinea","Lola",336,0.04431
+"Uganda","Jinja",337,0.04371
+"Uganda","Kalungu",338,0.04362
+"Malawi","Chikwawa",339,0.04338
+"Togo","Kara",340,0.04331
+"Rwanda","Northern",341,0.04328
+"Kenya","Nyanza",342,0.04302
+"Burkina Faso","Ioba",343,0.04293
+"Malawi","Mzimba",344,0.0429
+"Sierra Leone","Eastern",345,0.04257
+"Burkina Faso","Loroum",346,0.04243
+"Republic of the Congo","Kouilou",347,0.04224
+"Uganda","Mityana",348,0.04217
+"Ivory Coast","Cavally",349,0.04171
+"Lesotho","Mohale's Hoek",350,0.04147
+"Senegal","Kolda",351,0.04105
+"Burkina Faso","Ziro",352,0.04104
+"Rwanda","Western",353,0.04092
+"Guinea","Kindia",354,0.04089
+"Togo","Savanes",355,0.0408
+"Somalia","Shabeellaha Hoose",356,0.04069
+"Tunisia","Tozeur",357,0.04064
+"Zimbabwe","Mashonaland Central",358,0.04056
+"Sudan","White Nile",359,0.04054
+"Benin","Zou",360,0.04041
+"Ghana","Northern",361,0.0403
+"Burkina Faso","Namentenga",362,0.03989
+"Algeria","Djelfa",363,0.03967
+"Sierra Leone","Southern",364,0.03911
+"Somalia","Shabeellaha Dhexe",365,0.03903
+"Uganda","Rukungiri",366,0.03893
+"Uganda","Kanungu",367,0.03892
+"Morocco","Oriental",368,0.03882
+"Uganda","Isingiro",369,0.03849
+"Benin","Alibori",370,0.03838
+"Burundi","Cankuzo",371,0.03789
+"Uganda","Kisoro",372,0.03765
+"Namibia","Oshana",373,0.03745
+"Uganda","Kamuli",374,0.03709
+"Benin","Atakora",375,0.03703
+"Cameroon","Ouest",376,0.03692
+"Burkina Faso","Kossi",377,0.0363
+"Uganda","Mbale",378,0.03614
+"Uganda","Kabarole",379,0.03597
+"Botswana","Kgatleng",380,0.03569
+"Uganda","Ibanda",381,0.03555
+"Kenya","Central",382,0.03547
+"Mozambique","Maputo",383,0.03546
+"Uganda","Bundibugyo",384,0.03534
+"United Republic of Tanzania","Mtwara",385,0.03517
+"Liberia","River Cess",386,0.03499
+"Uganda","Luuka",387,0.03492
+"Guinea Bissau","Bafatá",388,0.03448
+"Nigeria","Adamawa",389,0.03443
+"Democratic Republic of the Congo","Bas-Congo",390,0.03409
+"Algeria","Laghouat",391,0.03403
+"Mauritania","Guidimaka",392,0.03385
+"Kenya","Western",393,0.0338
+"Sudan","Sennar",394,0.03321
+"Malawi","Nsanje",395,0.03313
+"Togo","Plateaux",396,0.03294
+"Nigeria","Rivers",397,0.03274
+"Senegal","Saint-Louis",398,0.03272
+"Equatorial Guinea","Centro Sur",399,0.03237
+"Ethiopia","Tigray",400,0.03188
+"Uganda","Kyenjojo",401,0.0317
+"Zambia","Lusaka",402,0.03146
+"Togo","Centre",403,0.03082
+"Lesotho","Butha-Buthe",404,0.03072
+"Sierra Leone","Northern",405,0.03063
+"Mali","Kayes",406,0.03037
+"Malawi","Likoma",407,0.03015
+"Uganda","Buikwe",408,0.03009
+"Nigeria","Kaduna",409,0.03004
+"United Republic of Tanzania","Pwani",410,0.02998
+"Uganda","Namayingo",411,0.02973
+"Zimbabwe","Mashonaland West",412,0.0297
+"Uganda","Sironko",413,0.02934
+"Senegal","Tambacounda",414,0.02933
+"Nigeria","Katsina",415,0.02931
+"Zimbabwe","Manicaland",416,0.02905
+"Lesotho","Quthing",417,0.02861
+"Burkina Faso","Bougouriba",418,0.02854
+"Uganda","Bukomansimbi",419,0.02818
+"Guinea","Kérouané",420,0.02816
+"Guinea","Télimélé",421,0.02814
+"Egypt","Al Jizah",422,0.02811
+"Guinea","Kissidougou",423,0.028
+"Angola","Huambo",424,0.0279
+"Malawi","Nkhotakota",425,0.02737
+"Angola","Uíge",426,0.02726
+"Nigeria","Niger",427,0.02724
+"Uganda","Kayunga",428,0.02709
+"Madagascar","Sava",429,0.02699
+"United Republic of Tanzania","Geita",430,0.02591
+"Guinea","Mali",431,0.0259
+"Democratic Republic of the Congo","Kasaï-Occidental",432,0.02563
+"Uganda","Masaka",433,0.02547
+"Namibia","Omusati",434,0.02545
+"Nigeria","Jigawa",435,0.02525
+"Liberia","Sinoe",436,0.02517
+"Uganda","Maracha",437,0.02498
+"Burkina Faso","Noumbiel",438,0.02476
+"Burkina Faso","Poni",439,0.02469
+"Madagascar","Vakinankaratra",440,0.02448
+"Uganda","Kyegegwa",441,0.02443
+"Liberia","Grand Cape Mount",442,0.02432
+"Egypt","Aswan",443,0.0241
+"United Republic of Tanzania","Shinyanga",444,0.02396
+"Burkina Faso","Yagha",445,0.02371
+"Angola","Cuanza Norte",446,0.02369
+"Malawi","Rumphi",447,0.02337
+"Nigeria","Kwara",448,0.02323
+"Nigeria","Borno",449,0.023
+"United Republic of Tanzania","Mwanza",450,0.02295
+"Guinea","Boke",451,0.02291
+"Libya","Al Qubbah",452,0.02258
+"Lesotho","Qacha's Nek",453,0.02245
+"Ethiopia","Oromiya",454,0.02242
+"Uganda","Buvuma",455,0.02228
+"Ethiopia","Amhara",456,0.02213
+"Burkina Faso","Gnagna",457,0.02208
+"Ethiopia","Southern Nations, Nationalities and Peoples",458,0.02191
+"Cameroon","Littoral",459,0.02129
+"Namibia","Oshikoto",460,0.02129
+"United Republic of Tanzania","Mbeya",461,0.02108
+"Uganda","Mubende",462,0.02105
+"Mauritania","Gorgol",463,0.02103
+"Uganda","Kibale",464,0.02102
+"Cameroon","Extrême-Nord",465,0.021
+"Tunisia","Kebili",466,0.0209
+"Uganda","Nakaseke",467,0.02083
+"Uganda","Gomba",468,0.02082
+"Madagascar","Bongolava",469,0.0208
+"Mozambique","Cabo Delgado",470,0.02072
+"Democratic Republic of the Congo","Kasaï-Oriental",471,0.02067
+"Malawi","Mwanza",472,0.02062
+"Malawi","Chitipa",473,0.02028
+"Uganda","Kamwenge",474,0.02027
+"Uganda","Rakai",475,0.02022
+"United Republic of Tanzania","Rukwa",476,0.02017
+"Angola","Cuanza Sul",477,0.02014
+"Malawi","Nkhata Bay",478,0.02002
+"Uganda","Buhweju",479,0.01997
+"Nigeria","Bauchi",480,0.01976
+"Nigeria","Delta",481,0.01976
+"Uganda","Mbarara",482,0.0197
+"Namibia","Caprivi",483,0.01959
+"Uganda","Namutumba",484,0.01955
+"Eritrea","Semenawi Keyih Bahri",485,0.01948
+"Madagascar","Analanjirofo",486,0.01942
+"Democratic Republic of the Congo","Bandundu",487,0.01926
+"Madagascar","Sofia",488,0.01915
+"Republic of the Congo","Brazzaville",489,0.0191
+"United Republic of Tanzania","Kagera",490,0.01906
+"Burkina Faso","Gourma",491,0.01902
+"Malawi","Chitipa",492,0.0187
+"Uganda","Kaliro",493,0.01857
+"Nigeria","Kogi",494,0.0185
+"Madagascar","Alaotra-Mangoro",495,0.01822
+"Liberia","Maryland",496,0.01822
+"Uganda","Lwengo",497,0.01795
+"Eritrea","Gash Barka",498,0.0175
+"Guinea","Faranah",499,0.01749
+"Uganda","Moyo",500,0.01748
+"Nigeria","Yobe",501,0.01747
+"Morocco","Guelmim - Es-Semara",502,0.01739
+"Somalia","Mudug",503,0.01737
+"United Republic of Tanzania","Njombe",504,0.01733
+"United Republic of Tanzania","Iringa",505,0.0173
+"Guinea","Mamou",506,0.01726
+"Zambia","Southern",507,0.01723
+"Zambia","Central",508,0.01721
+"Guinea","Koubia",509,0.01702
+"Botswana","Southern",510,0.01693
+"Senegal","Matam",511,0.01677
+"Benin","Collines",512,0.01653
+"United Republic of Tanzania","Tanga",513,0.01629
+"Somalia","Galguduud",514,0.01625
+"Uganda","Buyende",515,0.01621
+"Mozambique","Nampula",516,0.0162
+"Libya","Surt",517,0.01606
+"Kenya","Coast",518,0.01596
+"Uganda","Kiboga",519,0.01591
+"Liberia","Lofa",520,0.01578
+"Nigeria","Oyo",521,0.01578
+"Guinea","Dabola",522,0.01578
+"Liberia","Grand Gedeh",523,0.01568
+"Uganda","Hoima",524,0.01567
+"Somalia","Jubbada Dhexe",525,0.01564
+"Uganda","Nebbi",526,0.01556
+"Somalia","Hiiraan",527,0.01553
+"Angola","Malanje",528,0.01551
+"South Africa","Northern Cape",529,0.0155
+"Lesotho","Mokhotlong",530,0.01548
+"Uganda","Manafwa",531,0.01547
+"Namibia","Kavango",532,0.01536
+"Libya","Sabha",533,0.01518
+"Nigeria","Federal Capital Territory",534,0.01515
+"Liberia","River Gee",535,0.01514
+"Algeria","El Oued",536,0.01469
+"Democratic Republic of the Congo","Kinshasa City",537,0.01466
+"Uganda","Kasese",538,0.01455
+"United Republic of Tanzania","Dodoma",539,0.0145
+"Uganda","Budaka",540,0.01449
+"Somalia","Gedo",541,0.01436
+"Madagascar","Haute Matsiatra",542,0.01434
+"Guinea","Kouroussa",543,0.01424
+"Lesotho","Thaba-Tseka",544,0.01416
+"Nigeria","Nassarawa",545,0.01415
+"United Republic of Tanzania","Kilimanjaro",546,0.01415
+"Angola","Cunene",547,0.01403
+"Zambia","Eastern",548,0.01402
+"Guinea","Kankan",549,0.01393
+"Nigeria","Plateau",550,0.01389
+"Uganda","Kibuku",551,0.01385
+"Nigeria","Ebonyi",552,0.01378
+"United Republic of Tanzania","Kigoma",553,0.01373
+"Zimbabwe","Midlands",554,0.01353
+"Burkina Faso","Séno",555,0.01341
+"Eritrea","Debub",556,0.01331
+"Madagascar","Atsinanana",557,0.01298
+"Democratic Republic of the Congo","Katanga",558,0.01286
+"Libya","Misratah",559,0.01278
+"Egypt","Janub Sina'",560,0.01272
+"Burkina Faso","Soum",561,0.01265
+"Angola","Bengo",562,0.01254
+"Libya","Wadi al Hayaa",563,0.0125
+"Sudan","River Nile",564,0.01231
+"Madagascar","Diana",565,0.0123
+"Madagascar","Amoron'i Mania",566,0.01228
+"Uganda","Tororo",567,0.01227
+"Nigeria","Cross River",568,0.01222
+"Sudan","Gedarif",569,0.0122
+"Uganda","Butaleja",570,0.012
+"Djibouti","Arta",571,0.01187
+"United Republic of Tanzania","Ruvuma",572,0.01185
+"Gabon","Wouleu-Ntem",573,0.01177
+"Sudan","Kassala",574,0.01174
+"Uganda","Sembabule",575,0.01168
+"Angola","Zaire",576,0.01164
+"Uganda","Kumi",577,0.01154
+"Uganda","Nakasongola",578,0.01152
+"Libya","Mizdah",579,0.01144
+"Botswana","Kweneng",580,0.01142
+"Madagascar","Vatovavy-Fitovinany",581,0.01135
+"Uganda","Zombo",582,0.01134
+"Somalia","Bari",583,0.01127
+"Niger","Tillabéri",584,0.01115
+"Democratic Republic of the Congo","Sud-Kivu",585,0.01114
+"Angola","Benguela",586,0.01113
+"Tunisia","Tataouine",587,0.01113
+"Liberia","Grand Kru",588,0.01107
+"Burkina Faso","Komondjari",589,0.01102
+"Madagascar","Anosy",590,0.01088
+"Zimbabwe","Matabeleland South",591,0.01085
+"Guinea Bissau","Gabú",592,0.01082
+"Mali","Mopti",593,0.01077
+"Uganda","Ntoroko",594,0.01055
+"Algeria","Naâma",595,0.0103
+"Djibouti","Ali Sabieh",596,0.01026
+"Mauritania","Brakna",597,0.01025
+"Uganda","Gulu",598,0.00999
+"Niger","Tahoua",599,0.00998
+"Namibia","Khomas",600,0.00995
+"Botswana","Central",601,0.00987
+"Burkina Faso","Oudalan",602,0.00985
+"Uganda","Pallisa",603,0.00984
+"Libya","Ghadamis",604,0.00981
+"Gabon","Estuaire",605,0.00972
+"Uganda","Kapchorwa",606,0.00958
+"Uganda","Bulambuli",607,0.00953
+"Guinea","Koundara",608,0.00953
+"Cameroon","Centre",609,0.0095
+"Cameroon","Nord-Ouest",610,0.00949
+"Cameroon","Nord",611,0.00947
+"Angola","Huíla",612,0.00932
+"United Republic of Tanzania","Simiyu",613,0.00927
+"Somalia","Bay",614,0.00925
+"Angola","Bié",615,0.00917
+"Djibouti","Tadjourah",616,0.00914
+"Somalia","Nugaal",617,0.00912
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+"Senegal","Kédougou",752,0.00219
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+"Egypt","Shamal Sina'",755,0.00208
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+"Chad","Mayo-Kebbi Est",758,0.00194
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+"Algeria","Adrar",760,0.00181
+"Chad","Mandoul",761,0.00176
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+"Gabon","Nyanga",772,0.00149
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+"Botswana","Ghanzi",775,0.00134
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+"Cameroon","Adamaoua",777,0.00125
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+"Chad","Sila",792,0.00079
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+"Chad","Salamat",794,0.00065
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+"Algeria","Tindouf",799,5e-04
+"Uganda","Nakapiripirit",800,0.00049
+"Central African Republic","Haut-Mbomou",801,0.00047
+"Niger","Agadez",802,0.00046
+"Chad","Batha",803,0.00045
+"Niger","Diffa",804,0.00044
+"Libya","Al Kufrah",805,0.00043
+"Algeria","Tamanghasset",806,0.00043
+"Uganda","Katakwi",807,0.00042
+"Chad","Mayo-Kebbi Ouest",808,0.00038
+"Mauritania","Tiris Zemmour",809,0.00036
+"Chad","Tibesti",810,0.00036
+"Morocco","Oued el Dahab",811,0.00025
+"Cameroon","Sud",812,0.00023
+"Chad","Borkou",813,2e-04
+"Mali","Kidal",814,0.00019
+"Central African Republic","Nana-Grébizi",815,0.00018
+"Gabon","Ogooué-Lolo",816,0.00014
+"Uganda","Oyam",817,0.00011
+"Uganda","Ngora",818,1e-04
+"Chad","Lac",819,8e-05
+"Central African Republic","Vakaga",820,6e-05
+"Central African Republic","Bamingui-Bangoran",821,5e-05
+"Uganda","Soroti",822,1e-05
+"Uganda","Moroto",823,0
+"Djibouti","Djibouti",824,0
+"Seychelles","Outer Islands",825,0
+"Comoros","Moûhîlî",826,0
+"Comoros","Andjouân",827,0
+"Comoros","Andjazîdja",828,0
+"Equatorial Guinea","Annobón",829,0
+"Sao Tome and Principe","São Tomé",830,0
+"Sao Tome and Principe","Príncipe",831,0
+"Uganda","Lira",832,-4e-05
+"Central African Republic","Ouaka",833,-7e-05
+"Chad","Barh El Gazel",834,-1e-04
+"Uganda","Amudat",835,-0.00012
+"Uganda","Agago",836,-0.00013
+"Central African Republic","Haute-Kotto",837,-0.00016
+"Chad","Kanem",838,-0.00018
+"Central African Republic","Mambéré-Kadéï",839,-0.00023
+"Republic of the Congo","Likouala",840,-0.00059
+"Uganda","Bukwa",841,-0.00062
+"Republic of the Congo","Sangha",842,-0.00083
+"Democratic Republic of the Congo","Équateur",843,-0.00156
+"Uganda","Otuke",844,-0.0017
+"Niger","Zinder",845,-0.00189
+"Uganda","Abim",846,-0.00248
+"Uganda","Kween",847,-0.00263
+"Uganda","Dokolo",848,-0.00416
+"Niger","Maradi",849,-0.00542
+"Uganda","Alebtong",850,-0.00578
+"Uganda","Kole",851,-0.00593
+"Cameroon","Est",852,-0.00641
+"Uganda","Amuria",853,-0.00875
+"Nigeria","Bayelsa",854,-0.00919
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requirements.txt

@@ -0,0 +1,14 @@
+shiny 
+shiny
+shinywidgets
+ipyleaflet
+ipywidgets
+numpy
+matplotlib 
+pandas
+plotly
+rasterio
+geopandas
+plotnine
+palettable
+

residual_plot.ipynb

@@ -0,0 +1,754 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>RegionID</th>\n",
+       "      <th>ClusterID</th>\n",
+       "      <th>lon</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>country.code.ISO.3166.alpha.3</th>\n",
+       "      <th>year.of.interview</th>\n",
+       "      <th>month.of.interview</th>\n",
+       "      <th>rural</th>\n",
+       "      <th>country</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>iwi_mean</th>\n",
+       "      <th>iwi_var</th>\n",
+       "      <th>iwi_skewness</th>\n",
+       "      <th>iwi_kurtosis</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>AO.Estavel Mesoendemica</td>\n",
+       "      <td>AO.Estavel Mesoendemica.51.1</td>\n",
+       "      <td>13.859255</td>\n",
+       "      <td>-12.169283</td>\n",
+       "      <td>24</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>12</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>Angola</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>0.632356</td>\n",
+       "      <td>51.530785</td>\n",
+       "      <td>-1.136435</td>\n",
+       "      <td>4.376306</td>\n",
+       "      <td>POINT (13.85925 -12.16928)</td>\n",
+       "      <td>2006-12-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>AO.Estavel Mesoendemica</td>\n",
+       "      <td>AO.Estavel Mesoendemica.51.1</td>\n",
+       "      <td>13.859255</td>\n",
+       "      <td>-12.169283</td>\n",
+       "      <td>24</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>Angola</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>0.632356</td>\n",
+       "      <td>51.530785</td>\n",
+       "      <td>-1.136435</td>\n",
+       "      <td>4.376306</td>\n",
+       "      <td>POINT (13.85925 -12.16928)</td>\n",
+       "      <td>2006-11-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>AO.Estavel Mesoendemica</td>\n",
+       "      <td>AO.Estavel Mesoendemica.51.2</td>\n",
+       "      <td>13.845458</td>\n",
+       "      <td>-12.167753</td>\n",
+       "      <td>24</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>Angola</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>0.596262</td>\n",
+       "      <td>99.839389</td>\n",
+       "      <td>-1.121912</td>\n",
+       "      <td>3.897659</td>\n",
+       "      <td>POINT (13.84546 -12.16775)</td>\n",
+       "      <td>2006-11-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>AO.Estavel Mesoendemica</td>\n",
+       "      <td>AO.Estavel Mesoendemica.51.3</td>\n",
+       "      <td>13.607121</td>\n",
+       "      <td>-12.747312</td>\n",
+       "      <td>24</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>Angola</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>0.640985</td>\n",
+       "      <td>96.416849</td>\n",
+       "      <td>-0.199522</td>\n",
+       "      <td>2.731744</td>\n",
+       "      <td>POINT (13.60712 -12.74731)</td>\n",
+       "      <td>2006-11-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>AO.Estavel Mesoendemica</td>\n",
+       "      <td>AO.Estavel Mesoendemica.51.4</td>\n",
+       "      <td>14.060841</td>\n",
+       "      <td>-11.502919</td>\n",
+       "      <td>24</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>12</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>Angola</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>0.505828</td>\n",
+       "      <td>359.699498</td>\n",
+       "      <td>-0.507887</td>\n",
+       "      <td>2.371137</td>\n",
+       "      <td>POINT (14.06084 -11.50292)</td>\n",
+       "      <td>2006-12-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66024</th>\n",
+       "      <td>66025</td>\n",
+       "      <td>ZM.Lusaka</td>\n",
+       "      <td>ZM.Lusaka.71.541</td>\n",
+       "      <td>28.370067</td>\n",
+       "      <td>-15.416081</td>\n",
+       "      <td>894</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Urban</td>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>0.530495</td>\n",
+       "      <td>201.920771</td>\n",
+       "      <td>-0.296764</td>\n",
+       "      <td>3.270728</td>\n",
+       "      <td>POINT (28.37007 -15.41608)</td>\n",
+       "      <td>2018-09-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66025</th>\n",
+       "      <td>66026</td>\n",
+       "      <td>ZM.Muchinga</td>\n",
+       "      <td>ZM.Muchinga.71.542</td>\n",
+       "      <td>32.946955</td>\n",
+       "      <td>-11.150453</td>\n",
+       "      <td>894</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>0.179347</td>\n",
+       "      <td>70.917266</td>\n",
+       "      <td>0.186924</td>\n",
+       "      <td>2.157706</td>\n",
+       "      <td>POINT (32.94696 -11.15045)</td>\n",
+       "      <td>2018-09-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66026</th>\n",
+       "      <td>66027</td>\n",
+       "      <td>ZM.Lusaka</td>\n",
+       "      <td>ZM.Lusaka.71.543</td>\n",
+       "      <td>28.299570</td>\n",
+       "      <td>-15.769918</td>\n",
+       "      <td>894</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>12</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>0.340004</td>\n",
+       "      <td>305.619875</td>\n",
+       "      <td>0.645395</td>\n",
+       "      <td>2.523493</td>\n",
+       "      <td>POINT (28.29957 -15.76992)</td>\n",
+       "      <td>2018-12-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66027</th>\n",
+       "      <td>66028</td>\n",
+       "      <td>ZM.Central</td>\n",
+       "      <td>ZM.Central.71.544</td>\n",
+       "      <td>30.039495</td>\n",
+       "      <td>-12.876392</td>\n",
+       "      <td>894</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>0.135886</td>\n",
+       "      <td>59.794252</td>\n",
+       "      <td>0.061590</td>\n",
+       "      <td>1.959555</td>\n",
+       "      <td>POINT (30.0395 -12.87639)</td>\n",
+       "      <td>2018-09-15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66028</th>\n",
+       "      <td>66029</td>\n",
+       "      <td>ZM.Muchinga</td>\n",
+       "      <td>ZM.Muchinga.71.545</td>\n",
+       "      <td>32.608905</td>\n",
+       "      <td>-9.421954</td>\n",
+       "      <td>894</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>12</td>\n",
+       "      <td>Rural</td>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>0.205844</td>\n",
+       "      <td>261.427106</td>\n",
+       "      <td>1.139779</td>\n",
+       "      <td>3.475082</td>\n",
+       "      <td>POINT (32.6089 -9.42195)</td>\n",
+       "      <td>2018-12-15</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>65655 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unnamed: 0                 RegionID                     ClusterID  \\\n",
+       "0               1  AO.Estavel Mesoendemica  AO.Estavel Mesoendemica.51.1   \n",
+       "1               2  AO.Estavel Mesoendemica  AO.Estavel Mesoendemica.51.1   \n",
+       "2               3  AO.Estavel Mesoendemica  AO.Estavel Mesoendemica.51.2   \n",
+       "3               4  AO.Estavel Mesoendemica  AO.Estavel Mesoendemica.51.3   \n",
+       "4               5  AO.Estavel Mesoendemica  AO.Estavel Mesoendemica.51.4   \n",
+       "...           ...                      ...                           ...   \n",
+       "66024       66025                ZM.Lusaka              ZM.Lusaka.71.541   \n",
+       "66025       66026              ZM.Muchinga            ZM.Muchinga.71.542   \n",
+       "66026       66027                ZM.Lusaka              ZM.Lusaka.71.543   \n",
+       "66027       66028               ZM.Central             ZM.Central.71.544   \n",
+       "66028       66029              ZM.Muchinga            ZM.Muchinga.71.545   \n",
+       "\n",
+       "             lon        lat  country.code.ISO.3166.alpha.3  year.of.interview  \\\n",
+       "0      13.859255 -12.169283                             24               2006   \n",
+       "1      13.859255 -12.169283                             24               2006   \n",
+       "2      13.845458 -12.167753                             24               2006   \n",
+       "3      13.607121 -12.747312                             24               2006   \n",
+       "4      14.060841 -11.502919                             24               2006   \n",
+       "...          ...        ...                            ...                ...   \n",
+       "66024  28.370067 -15.416081                            894               2018   \n",
+       "66025  32.946955 -11.150453                            894               2018   \n",
+       "66026  28.299570 -15.769918                            894               2018   \n",
+       "66027  30.039495 -12.876392                            894               2018   \n",
+       "66028  32.608905  -9.421954                            894               2018   \n",
+       "\n",
+       "       month.of.interview  rural country continent  iwi_mean     iwi_var  \\\n",
+       "0                      12  Urban  Angola    Africa  0.632356   51.530785   \n",
+       "1                      11  Urban  Angola    Africa  0.632356   51.530785   \n",
+       "2                      11  Urban  Angola    Africa  0.596262   99.839389   \n",
+       "3                      11  Urban  Angola    Africa  0.640985   96.416849   \n",
+       "4                      12  Urban  Angola    Africa  0.505828  359.699498   \n",
+       "...                   ...    ...     ...       ...       ...         ...   \n",
+       "66024                   9  Urban  Zambia    Africa  0.530495  201.920771   \n",
+       "66025                   9  Rural  Zambia    Africa  0.179347   70.917266   \n",
+       "66026                  12  Rural  Zambia    Africa  0.340004  305.619875   \n",
+       "66027                   9  Rural  Zambia    Africa  0.135886   59.794252   \n",
+       "66028                  12  Rural  Zambia    Africa  0.205844  261.427106   \n",
+       "\n",
+       "       iwi_skewness  iwi_kurtosis                    geometry       date  \n",
+       "0         -1.136435      4.376306  POINT (13.85925 -12.16928) 2006-12-15  \n",
+       "1         -1.136435      4.376306  POINT (13.85925 -12.16928) 2006-11-15  \n",
+       "2         -1.121912      3.897659  POINT (13.84546 -12.16775) 2006-11-15  \n",
+       "3         -0.199522      2.731744  POINT (13.60712 -12.74731) 2006-11-15  \n",
+       "4         -0.507887      2.371137  POINT (14.06084 -11.50292) 2006-12-15  \n",
+       "...             ...           ...                         ...        ...  \n",
+       "66024     -0.296764      3.270728  POINT (28.37007 -15.41608) 2018-09-15  \n",
+       "66025      0.186924      2.157706  POINT (32.94696 -11.15045) 2018-09-15  \n",
+       "66026      0.645395      2.523493  POINT (28.29957 -15.76992) 2018-12-15  \n",
+       "66027      0.061590      1.959555   POINT (30.0395 -12.87639) 2018-09-15  \n",
+       "66028      1.139779      3.475082    POINT (32.6089 -9.42195) 2018-12-15  \n",
+       "\n",
+       "[65655 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np \n",
+    "import pandas as pd \n",
+    "import json \n",
+    "from ipyleaflet import Map, GeoJSON,Heatmap\n",
+    "import matplotlib.pyplot as plt \n",
+    "from fetch_data import fetch_data\n",
+    "import geopandas as gp \n",
+    "import seaborn as sns \n",
+    "\n",
+    "\n",
+    "geo_json_data  = fetch_data(\"data/ground_truth/ground_truth.geojson\")\n",
+    "\n",
+    "ground_truth_df = fetch_data('data/ground_truth/ground_truth_R.csv')\n",
+    "\n",
+    "gdf = gp.GeoDataFrame(ground_truth_df, geometry=gp.points_from_xy(ground_truth_df['lon'], ground_truth_df['lat']), crs=\"EPSG:4326\")\n",
+    "gdf['iwi_mean'] = gdf['iwi_mean']/100 #scale IWIs by 100 \n",
+    "gdf = gdf[gdf['month.of.interview'] > 0]\n",
+    "gdf['date'] = pd.to_datetime(dict(year = gdf['year.of.interview'], month = gdf['month.of.interview'], day = 15))\n",
+    "\n",
+    "# fiilter data by the start and end year for a single band \n",
+    "def create_band(gdf, start, end):\n",
+    "    gdf = gdf[['country','year.of.interview','date','iwi_mean','geometry']]\n",
+    "    return gdf[(gdf['year.of.interview'] >= start) & (gdf['year.of.interview'] <= end)]\n",
+    "\n",
+    "# create a dictionary storing each band's data based on their year range \n",
+    "empty_dict = {}\n",
+    "def create_dict(ground_truth_dict):\n",
+    "    start = 1990 \n",
+    "    for i in range(1,11): \n",
+    "        end = start +2 \n",
+    "        band = create_band(gdf, start, end)\n",
+    "        ground_truth_dict[f'band_{i}'] = band \n",
+    "        temp = end \n",
+    "        start = temp +1 \n",
+    "    return ground_truth_dict\n",
+    "\n",
+    "ground_truth_dict = create_dict(empty_dict)\n",
+    "\n",
+    "gdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Band 1 upload complete!\n",
+      "Band 2 upload complete!\n",
+      "Band 3 upload complete!\n",
+      "Band 4 upload complete!\n",
+      "Band 5 upload complete!\n",
+      "Band 6 upload complete!\n",
+      "Band 7 upload complete!\n",
+      "Band 8 upload complete!\n",
+      "Band 9 upload complete!\n",
+      "Band 10 upload complete!\n"
+     ]
+    }
+   ],
+   "source": [
+    "#load data for each band \n",
+    "band_dict= {}\n",
+    "for i in range(1,11): \n",
+    "    band_json = fetch_data(f'data/band_geojson/band_{i}.geojson')\n",
+    "    band_dict[f'band_{i}'] = band_json\n",
+    "    print(f'Band {i} upload complete!')\n",
+    "\n",
+    "with open('data/band_json.geojson', 'w') as w:\n",
+    "    json.dump(band_dict, w, indent=4)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing Band 1...\n",
+      "Processing Band 2...\n",
+      "Processing Band 3...\n",
+      "Processing Band 4...\n",
+      "Processing Band 5...\n",
+      "Processing Band 6...\n",
+      "Processing Band 7...\n",
+      "Processing Band 8...\n",
+      "Processing Band 9...\n",
+      "Processing Band 10...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year.of.interview</th>\n",
+       "      <th>date</th>\n",
+       "      <th>iwi_mean</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>index_right</th>\n",
+       "      <th>iwi_pred</th>\n",
+       "      <th>residual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2364</th>\n",
+       "      <td>Cameroon</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>1991-08-15</td>\n",
+       "      <td>0.210099</td>\n",
+       "      <td>POINT (14.9 12.3833)</td>\n",
+       "      <td>30259</td>\n",
+       "      <td>0.239990</td>\n",
+       "      <td>0.029891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2365</th>\n",
+       "      <td>Cameroon</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>1991-07-15</td>\n",
+       "      <td>0.254330</td>\n",
+       "      <td>POINT (14.15 11.05)</td>\n",
+       "      <td>37563</td>\n",
+       "      <td>0.240723</td>\n",
+       "      <td>-0.013607</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2366</th>\n",
+       "      <td>Cameroon</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>1991-07-15</td>\n",
+       "      <td>0.174986</td>\n",
+       "      <td>POINT (14.15 10.8667)</td>\n",
+       "      <td>38528</td>\n",
+       "      <td>0.166138</td>\n",
+       "      <td>-0.008848</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2367</th>\n",
+       "      <td>Cameroon</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>1991-07-15</td>\n",
+       "      <td>0.174986</td>\n",
+       "      <td>POINT (13.8833 10.8667)</td>\n",
+       "      <td>38527</td>\n",
+       "      <td>0.150146</td>\n",
+       "      <td>-0.024839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2368</th>\n",
+       "      <td>Cameroon</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>1991-07-15</td>\n",
+       "      <td>0.178511</td>\n",
+       "      <td>POINT (13.8 10.75)</td>\n",
+       "      <td>39175</td>\n",
+       "      <td>0.179443</td>\n",
+       "      <td>0.000933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66019</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>2018-10-15</td>\n",
+       "      <td>0.348706</td>\n",
+       "      <td>POINT (28.09312 -15.34204)</td>\n",
+       "      <td>108281</td>\n",
+       "      <td>0.504883</td>\n",
+       "      <td>0.156177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66022</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>2018-08-15</td>\n",
+       "      <td>0.547186</td>\n",
+       "      <td>POINT (28.34869 -15.39249)</td>\n",
+       "      <td>108374</td>\n",
+       "      <td>0.556641</td>\n",
+       "      <td>0.009455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66023</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>2018-08-15</td>\n",
+       "      <td>0.530495</td>\n",
+       "      <td>POINT (28.37007 -15.41608)</td>\n",
+       "      <td>108455</td>\n",
+       "      <td>0.439209</td>\n",
+       "      <td>-0.091286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66024</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>2018-09-15</td>\n",
+       "      <td>0.530495</td>\n",
+       "      <td>POINT (28.37007 -15.41608)</td>\n",
+       "      <td>108455</td>\n",
+       "      <td>0.439209</td>\n",
+       "      <td>-0.091286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66028</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>2018-12-15</td>\n",
+       "      <td>0.205844</td>\n",
+       "      <td>POINT (32.6089 -9.42195)</td>\n",
+       "      <td>101268</td>\n",
+       "      <td>0.182495</td>\n",
+       "      <td>-0.023349</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>52102 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        country  year.of.interview       date  iwi_mean  \\\n",
+       "2364   Cameroon               1991 1991-08-15  0.210099   \n",
+       "2365   Cameroon               1991 1991-07-15  0.254330   \n",
+       "2366   Cameroon               1991 1991-07-15  0.174986   \n",
+       "2367   Cameroon               1991 1991-07-15  0.174986   \n",
+       "2368   Cameroon               1991 1991-07-15  0.178511   \n",
+       "...         ...                ...        ...       ...   \n",
+       "66019    Zambia               2018 2018-10-15  0.348706   \n",
+       "66022    Zambia               2018 2018-08-15  0.547186   \n",
+       "66023    Zambia               2018 2018-08-15  0.530495   \n",
+       "66024    Zambia               2018 2018-09-15  0.530495   \n",
+       "66028    Zambia               2018 2018-12-15  0.205844   \n",
+       "\n",
+       "                         geometry  index_right  iwi_pred  residual  \n",
+       "2364         POINT (14.9 12.3833)        30259  0.239990  0.029891  \n",
+       "2365          POINT (14.15 11.05)        37563  0.240723 -0.013607  \n",
+       "2366        POINT (14.15 10.8667)        38528  0.166138 -0.008848  \n",
+       "2367      POINT (13.8833 10.8667)        38527  0.150146 -0.024839  \n",
+       "2368           POINT (13.8 10.75)        39175  0.179443  0.000933  \n",
+       "...                           ...          ...       ...       ...  \n",
+       "66019  POINT (28.09312 -15.34204)       108281  0.504883  0.156177  \n",
+       "66022  POINT (28.34869 -15.39249)       108374  0.556641  0.009455  \n",
+       "66023  POINT (28.37007 -15.41608)       108455  0.439209 -0.091286  \n",
+       "66024  POINT (28.37007 -15.41608)       108455  0.439209 -0.091286  \n",
+       "66028    POINT (32.6089 -9.42195)       101268  0.182495 -0.023349  \n",
+       "\n",
+       "[52102 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#load all data at once and perform spatial join for each band \n",
+    "with open('data/band_json.geojson') as d: \n",
+    "    band_data = json.load(d)\n",
+    "\n",
+    "joined_gdf_list = []\n",
+    "for i in range(1, 11):\n",
+    "    print(f'Processing Band {i}...')\n",
+    "    pred_gdf = gp.GeoDataFrame.from_features(band_data[f'band_{i}'], crs = \"EPSG:4326\")\n",
+    "    pred_gdf = pred_gdf.rename(columns={'IWI':'iwi_pred'})\n",
+    "\n",
+    "    joined_gdf = gp.sjoin(ground_truth_dict[f'band_{i}'], pred_gdf, predicate='intersects')\n",
+    "    joined_gdf['residual'] = joined_gdf['iwi_pred'] - joined_gdf['iwi_mean']\n",
+    "    joined_gdf_list.append(joined_gdf)\n",
+    "\n",
+    "\n",
+    "#join all the dataframes together into a single dataframe \n",
+    "full_gdf = pd.concat(joined_gdf_list)\n",
+    "\n",
+    "full_gdf\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2364     Cameroon\n",
+       "2365     Cameroon\n",
+       "2366     Cameroon\n",
+       "2367     Cameroon\n",
+       "2368     Cameroon\n",
+       "           ...   \n",
+       "66019      Zambia\n",
+       "66022      Zambia\n",
+       "66023      Zambia\n",
+       "66024      Zambia\n",
+       "66028      Zambia\n",
+       "Name: country, Length: 52102, dtype: object"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#rename two-word countries to standardized names \n",
+    "full_gdf['country'].replace('Côte d’Ivoire', 'Ivory Coast')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#write to csv \n",
+    "full_gdf.to_csv('data/residual_by_country.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_country(country_name, gdf):\n",
+    "    gdf = gdf[gdf['country'] == country_name]\n",
+    "    modified_xticks = gdf['date'].dt.to_period('M')\n",
+    "    plt.figure(figsize=(10,6))\n",
+    "    sns.boxplot(x=modified_xticks, y=gdf['residual'])\n",
+    "    plt.xlabel('date')\n",
+    "    plt.xticks(rotation=90)\n",
+    "    plt.ylabel('iwi_value')\n",
+    "    plt.title(f'IWI Residual Plot For {country_name}')\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "\n",
+    "plot_country('Angola', full_gdf)\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "myenv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

residuals.py

@@ -0,0 +1,32 @@
+import matplotlib.pyplot as plt 
+import seaborn as sns
+import pandas as pd 
+import geopandas as gp
+import numpy as np
+
+def get_residual_plot(country_name, gdf): 
+    fig, ax = plt.subplots(1,1,figsize=(10,6))
+    if country_name is None:
+        ax.text(0.5, 0.5, "Click on the map to see the IWI time-series here.",
+                horizontalalignment='center', verticalalignment='center',
+                transform=ax.transAxes, fontsize=14)
+        
+    elif gdf['country'].str.contains(country_name).any():
+        gdf = gdf[gdf['country'] == country_name]
+        gdf['date'] = pd.to_datetime(gdf['date'])
+        modified_xticks = gdf['date'].dt.to_period('M')
+        ticks_unique = np.unique(modified_xticks)
+        sns.boxplot(x=modified_xticks, y=gdf['residual'], ax=ax)
+        ax.set_xlabel('date')
+        ax.set_xticks(range(len(ticks_unique)))
+        ax.set_xticklabels(labels = ticks_unique, rotation=45)
+        ax.set_ylabel('iwi_value')
+        ax.set_title(f'IWI Residual Plot For {country_name}')
+        ax.grid(True, linestyle='--', alpha=0.7)
+        plt.tight_layout()
+        return fig
+
+    else:
+        ax.text(0.5, 0.5, "No country data available. Please select another country.",
+                    horizontalalignment='center', verticalalignment='center',
+                    transform=ax.transAxes, fontsize=14)