Cannot access share point

"The difference in arrival vs. departure draught is a key calculation to estimate trade. However, the difference between outgoing and incoming draft is only identified in a subset of port calls. For the remaining routes, we apply the back-propagation technique, searching for the arrival draft at the next port visited."

Is " "draft" supposed to be "draught" here?

Apart from monthly temperature and rainfall data, was there other data used. Because the data section explains that there are a number of datasets that were used.

Currently, the package does not include dependencies adequately.

Cannot Access project sharepoint.

Alternative datasets as proxies for population growth and density in Lebanon

Introduction

This notebook is a proof of concept for using alternative datasets as proxies for population growth and density in Lebanon. The datasets used are:

• Facebook population density
• Kontur population density
• World Bank Data
  • Infrastructuree
  • Population
  • Urban Development
  • Gender

pacman::p_load(tidyverse, janitor, tmap, tmaptools, sf, mapview, GADMTools,
               hrbrthemes)

Importing and reading the data:

pop <- st_read("kontur_population_LB_20220630.gpkg") %>% st_transform(4326)

gender_lbn <- read_csv("gender_lbn.csv") %>% janitor::clean_names() %>% slice(-1)

urban <- read_csv("urban-development_lbn.csv") %>% janitor::clean_names() %>% slice(-1)

villa <- st_read("Lebanon_Villages.shp") %>% st_transform(4326)

births <- readxl::read_excel("birthsAndPregnanciesLBN.xlsx") %>% janitor::clean_names()

adm <- st_read("gadm41_LBN_2.json/gadm41_LBN_2.json")

Using tmap to visualize the population density in Lebanon:

 pop %>% tm_shape() + tm_polygons(col = "population", palette = "viridis", 
                                 style = "quantile", n = 5, 
                                 title = "Population", 
                                 legend.hist = TRUE, border.col = NULL,
                                 legend.is.portrait = FALSE
                                 )

Using geolocated data, we can visualize the villages in Lebanon:

tm2 <- tm_shape(villa) + tm_dots(
                              title = "Villages", col = "slateblue4",
                              legend.hist = TRUE, border.col = NULL,
                              legend.is.portrait = FALSE
                              ) +
  tm_layout(main.title = "Villages")

urban %>% filter(indicator_name %in% c("Urban population", 
                                       "Population in largest city")) %>% 
  mutate(value = as.numeric(value),
         year = as.numeric(year)) %>%
  ggplot(aes(x = year, y = value, group = indicator_name, color = indicator_name)) +
  geom_line() +
  geom_point() +
  labs(title = "Key urban indicators", x = "Year", y = "Population",
       caption = "Source: World Bank",
       color = NULL) +
  theme_minimal() +
  scale_y_continuous(labels = scales::comma) +
  theme_ipsum_rc() +
  scale_color_ipsum()  +
  theme(legend.position = "bottom")

biryears <- births %>% pivot_longer(!c(adm2_code, adm2_name, urban_or_rural)) %>% 
  filter(grepl("adjusted_births", name)) %>% 
  mutate(name = parse_number(name)) %>% 
  fuzzyjoin::stringdist_inner_join(adm, by = c("adm2_name" = "NAME_2"), max_dist = 2) %>%
  st_as_sf()


biryears %>% 
  group_by(urban_or_rural, name) %>%
  top_n(4, value) %>%
  ggplot(aes(x = name, y = value, color = adm2_name)) +
  geom_line() +
  geom_point() +
  labs(title = "Births", x = "Year", y = "Births",
       caption = "Source: World Bank",
       color = NULL) +
  theme_minimal() +
  scale_y_continuous(labels = scales::comma) +
  theme_ipsum_rc() +
  scale_color_ipsum()  +
  facet_wrap(~urban_or_rural) +
  theme(legend.position = "bottom")

refug <- jsonlite::fromJSON("https://data.unhcr.org/population/get/timeseries?widget_id=411213&geo_id=71&sv_id=4&population_collection=22&frequency=day&fromDate=2012-01-01")

refug$data$timeseries %>% 
  mutate(data_date = as.Date(data_date),
         individuals = as.numeric(individuals)) %>%
  ggplot(aes(x = data_date, y = individuals)) +
  geom_line() +
  geom_point() +
  labs(title = "Refugees", x = "Year", y = "Refugees",
       caption = "Source: UNHCR",
       color = NULL) +
  theme_minimal() +
  scale_y_continuous(labels = scales::comma) +
  theme_ipsum_rc() +
  scale_color_ipsum()  +
  theme(legend.position = "bottom")

Full ACLED database on Lebanon; At a later stage, match the regional conflict data with the regional NTL and population data. In addition to ACLED, are there any other conflict or social tension databases available for Lebanon?

Is there a part of the code that is missing?

Is "admin1Name" supposed to be "ADM0_PCODE"?

code error reference:
----> 5 df = monthly_no2_adm1.groupby(['admin1Name', 'date']).mean('NO2_column_number_density').reset_index()

KeyError: 'admin1Name'

Your issuDriverError: ../../data/shapefiles/lbn_adm_cdr_20200810/lbn_admbnda_adm2_cdr_20200810.shp: No such file or directory
e content here.

Where can i access the shapefile please?
code reference:
import geopandas as gpd

shapefile_path = '../../data/shapefiles/lbn_adm_cdr_20200810/lbn_admbnda_adm2_cdr_20200810.shp'
lebanon_adm2 = gpd.read_file(shapefile_path)

I followed the link provided to get the dataset, I opened the data archive hyperlink and saw a series of data sets with no clear description. Is it possible that the actual dataset name from the archive can be provided so I can find the dataset?

"Two datasets inform the analysis - NO2 dta obtained from Google Earth Engine and boundary files obtained from HDX."
Typo- "dta" should be "data"

Is there a way to estimate the share of refugees in population by region?

Are there data/datasets to quantity population migration within the country?

As of 0c48116, the Python package is not released properly. I'll open a PR to address this shortly adopting YY.MM.MICRO CalVer.

Unable to sign in to sharepoint.

error message below:

If that doesn't help, contact your support team and include these technical details:
Correlation ID: 45e316a1-4016-5000-16e5-d26067ee4baf
Date and Time: 3/20/2024 6:22:57 AM
URL: https://worldbankgroup.sharepoint.com/teams/DevelopmentDataPartnershipCommunity-WBGroup/Shared Documents/Forms/AllItems.aspx?cid=fccdf23e-94d5-48bf-b75d-0af291138bde&csf=1&e=Yvwh8r&FolderCTID=0x012000CFAB9FF0F938A64EBB297E7E16BDFCFD&id=%2Fteams%2FDevelopmentDataPartnershipCommunity-WBGroup%2FShared+Documents%2FProjects%2FData+Lab%2FLebanon+Economic+Analytics%2FData%2Ftrade&viewid=80cdadb3-8bb3-47ae-8b18-c1dd89c373c5&web=1
User: [email protected]
Issue Type: User not in directory.

Suggested comments

output_notebook() # Sets up Bokeh to display plots in Jupyter Notebook

bokeh.core.validation.silence(EMPTY_LAYOUT, True)# Suppress certain Bokeh warnings for a cleaner output
bokeh.core.validation.silence(MISSING_RENDERERS, True)# Suppress certain Bokeh warnings for a cleaner output

Grouping the data by administrative region and date, then calculate the mean of the NO2 column number density

df = monthly_no2_adm1.groupby(['admin1Name', 'date']).mean('NO2_column_number_density').reset_index()

Creating a line chart using a custom function get_line_chart

Parameters:

df: DataFrame containing the data

category: Column name for the categorical variable (administrative region in this case)

title: Title for the plot

source: Source of the data (e.g., 'Copernicus')

measure: The measure being plotted (NO2 column number density in this case)

line_chart = get_line_chart(df, category='admin1Name', title='Monthly Air Pollution in Lebanon by Admin 1', source='Copernicus', measure='NO2_column_number_density')

Display the line chart

show(line_chart)

Cannot access notebook.

Are there codes for EDA? I only see codes for visualizations. Is there a separate codebook/notebook?

If possible can written instructions be provided on how to navigate the websites where the datasets are ie. detailed steps to follow to get to the actual data. So far I only have access to the NO2 dataset which I downloaded from the Sharepoint(which is now not working). The other Datasets I have not been able to access. I am yet to try to reach out to the sources of the datasets however. I will try that too and see whether it helps. Thank you.

A few typos;
This notebook 'uses' analyses the NO2 data in Lebanon and presents the insights from the analysis. Two datasets inform the analysis - NO2 'dta' obtained from Google Earth Engine and boundary files obtained from HDX.

Are there codes for EDA? I only see codes for visualizations. Is there a separate codebook/notebook?

If possible can written instructions be provided on how to navigate the websites where the datasets are ie. detailed steps to follow to get to the actual data. So far I only have access to the NO2 dataset which I downloaded from the Sharepoint(which is now not working). The other Datasets I have not been able to access. I am yet to try to reach out to the sources of the datasets however. I will try that too and see whether it helps. Thank you.

initially encountered issues importing the bokeh library, solved it by running a pip install

Revert 73da1d0.

### Suggested comments

Creates a figure with 1 row and 2 columns of subplots, setting the figure size

fig, axs = plt.subplots(1, 2, figsize=(15, 5))

Sets font family for the entire plot

plt.rcParams["font.family"] = "Georgia"

Plots the total ask (total number of seats available) on the first subplot

axs[0].bar(x=inbound_flights_mena_monthly_ask['date'], # X values (dates)
height=inbound_flights_mena_monthly_ask['total_ask'], # Heights of bars (total ask)
width=20) # Width of bars

Plots the total payload (total weight of cargo) on the second subplot

axs[1].bar(x=inbound_flights_mena_monthly_ask['date'], # X values (dates)
height=inbound_flights_mena_monthly_ask['total_payload'], # Heights of bars (total payload)
width=20) # Width of bars

Loops through each subplot

for ax in axs:
# Hide the right and top spines (axis lines)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)

# Only shows ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')

# Adds gridlines to both major and minor ticks
ax.grid(which='both', linestyle='--', linewidth=0.5, color='gray', alpha=0.7)

# Sets titles for each subplot
axs[0].set_title('Average payload on Inbound Flights to Lebanon', font='Georgia', fontsize=12)
axs[1].set_title('Average Available Seat Kilometres on Inbound Flights to Lebanon', font='Georgia', fontsize=12)

# Sets x-label for each subplot
ax.set_xlabel('Month')

# Sets y-label for each subplot
axs[0].set_ylabel('Average payload (kg)')
axs[1].set_ylabel('Average Available Seat Kilometres')

Adds a subtitle to the first subplot

subtitle = 'Source: Global Aviation Dashboard (World Bank)'
axs[0].text(0, -0.15, subtitle, ha='left', va='center', transform=axs[0].transAxes,
fontsize=10, color='black', weight='normal')

Creates a figure with 2 rows and 1 column of subplots, setting the figure size

fig, axs = plt.subplots(2, 1, figsize=(10, 10))

Sets font family for the entire plot

plt.rcParams["font.family"] = "Georgia"

Plots the total number of seats on inbound flights monthly on the first subplot

axs[0].bar(x=inbound_flights_mena_monthly_all['date'], # X values (dates)
height=inbound_flights_mena_monthly_all['total_seats'], # Heights of bars (total seats)
width=20) # Width of bars

Plots the total number of seats on inbound flights yearly on the second subplot

axs[1].bar(x=inbound_flights_mena_yearly_all['date'], # X values (years)
height=inbound_flights_mena_yearly_all['total_seats'], # Heights of bars (total seats)
width=30) # Width of bars

Loops through each subplot

for ax in axs:
# Hides the right and top spines (axis lines)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)

# Only shows ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')

# Adds gridlines to both major and minor ticks
ax.grid(which='both', linestyle='--', linewidth=0.5, color='gray', alpha=0.7)

# Sets title for the first subplot
axs[0].set_title('Number of Seats on Inbound Flights to Lebanon (Monthly)', font='Georgia', fontsize=12)
# Sets title for the second subplot
axs[1].set_title('Number of Seats on Inbound Flights to Lebanon (Yearly)', font='Georgia', fontsize=12)

# Sets x-label for each subplot
ax.set_xlabel('Month' if ax == axs[0] else 'Year')

# Sets y-label for each subplot
ax.set_ylabel('Number of seats (in thousands)')

Adds a subtitle to the second subplot

subtitle = 'Source: Global Aviation Dashboard (World Bank)'
axs[1].text(0, -0.15, subtitle, ha='left', va='center', transform=axs[1].transAxes,
fontsize=10, color='black', weight='normal')

Imports necessary libraries

import numpy as np
import matplotlib.patches as mpatches

df: DataFrame containing data about top categories and seats

sorted_df: DataFrame sorted by date and total seats in descending order

df = top_categories_seats
sorted_df = df.sort_values(by=['date', 'total_seats'], ascending=[True, False])

Gets unique years

years = sorted_df['date'].unique()

Creats a figure and axis object

fig, ax = plt.subplots(figsize=(8, 6))
bar_width = 0.5 # Width of the bars
colors = ['#e6194b', '#3cb44b', '#ffe119', '#4363d8', '#f58231',
'#911eb4', '#42d4f4', '#f032e6', '#bfef45', '#fabed4'] # List of colors for the bars

Plots each category for each year

for i, year in enumerate(years):
bottom = np.zeros(len(years)) # Initializes the bottom values for stacked bars
sorted_df = df[df['date'] == year].sort_values(by='total_seats', ascending=False) # Sorts the data for the current year

# Gets unique categories for the current year
categories = sorted_df['origin_country'].unique()

# Plots each category for the current year
for j, category in enumerate(categories):
    # Gets the total seats for the current category and year
    value = sorted_df[(sorted_df['date'] == year) & (sorted_df['origin_country'] == category)]['total_seats'].sum()
    if value > 0:
        # Plots a bar for the current category and year, with stacking
        ax.bar(year, value, bar_width, bottom=bottom[i], color=country_color_map[category], label=category if i == 0 else "")
        bottom[i] += value  # Updates the bottom value for stacking

Sets axis labels

ax.set_xlabel('Year')
ax.set_ylabel('Number of seats (in thousands)')

Creates legend patches for each country

legend_patches = [mpatches.Patch(color=color, label=country) for country, color in country_color_map.items()]

Adds legend to the plot

ax.legend(handles=legend_patches, loc='upper center', frameon=False, bbox_to_anchor=(1.15, 1))

Customizes plot aesthetics

ax.spines['right'].set_visible(False) # Hides the right spine
ax.spines['top'].set_visible(False) # Hides the top spine
ax.yaxis.set_ticks_position('left') # Shows ticks only on the left spine
ax.xaxis.set_ticks_position('bottom') # Shows ticks only on the bottom spine
ax.grid(which='both', linestyle='--', linewidth=0.5, color='gray', alpha=0.7) # Add gridlines

Sets plot title and rotation for x-axis ticks

ax.set_title('Top 5 countries with number of inbound flight seats')
plt.xticks(rotation=0)

Adds a subtitle to the plot

subtitle = 'Source: Global Aviation Dashboard (World Bank)'
ax.text(0, -0.15, subtitle, ha='left', va='center', transform=ax.transAxes,
fontsize=10, color='black', weight='normal')

To overcome gaps in official demographic statistics, the team has turned to private sector data sources. In their book, “The Narrow Corridor,” Daron Acemoglu and James Robinson suggested that Lebanon has not conducted a population census since 1932. The task is to obtain and plot national and regional (as disaggregated as possible) population data, either monthly and annually.

I received the following error

.

"This notebook uses analyses the NO2 data in Lebanon and presents the insights from the analysis."
"uses" should be deleted.