Jews were the dominant religious group in the Israel-Palestine region at the beginning of the first millennia CE, and are the dominant religious group there today, however, there was a period of almost 2,000 years where most of the world's Jews were displaced from their spiritual homeland. Antiquity to the 20th century Jewish hegemony in the region began changing after a series of revolts against Roman rule led to mass expulsions and emigration. Roman control saw severe persecution of Jewish and Christian populations, but this changed when the Byzantine Empire adopted Christianity as its official religion in the 4th century. Christianity then dominated until the 7th century, when the Rashidun Caliphate (the first to succeed Muhammad) took control of the Levant. Control of region split between Christians and Muslims intermittently between the 11th and 13th centuries during the Crusades, although the population remained overwhelmingly Muslim. Zionism until today Through the Paris Peace Conference, the British took control of Palestine in 1920. The Jewish population began growing through the Zionist Movement after the 1880s, which sought to establish a Jewish state in Palestine. Rising anti-Semitism in Europe accelerated this in the interwar period, and in the aftermath of the Holocaust, many European Jews chose to leave the continent. The United Nations tried facilitating the foundation of separate Jewish and Arab states, yet neither side was willing to concede territory, leading to a civil war and a joint invasion from seven Arab states. Yet the Jews maintained control of their territory and took large parts of the proposed Arab territory, forming the Jewish-majority state of Israel in 1948, and acheiving a ceasefire the following year. Over 750,000 Palestinians were displaced as a result of this conflict, while most Jews from the Arab eventually fled to Israel. Since this time, Israel has become one of the richest and advanced countries in the world, however, Palestine has been under Israeli military occupation since the 1960s and there are large disparities in living standards between the two regions.

The total population in Palestine was forecast to continuously increase up to more than 5.4 million people by 2023. The current total population is estimated to amount to 5.48 million people in 2023, however these estimates have not yet been adjusted to account for the impact of the 2023 Israel-Hamas War.

Key information about State of Palestine (West Bank and Gaza) population

• The State of Palestine (West Bank and Gaza) population reached 5.9 million people in Jun 2026, compared with the previously reported figure of 5.7 million people in Jun 2025
• The data reached an all-time high of 5.9 million people in Jun 2026 and a record low of 0.9 million people in Jun 1951

*This indicator will be no longer available from March 2026, 4th onwards. CEIC has a plan to remove the non-standard frequency indicator after the standard frequency indicator has been added and be available on this table. CEIC extends history for annual Population. The Palestinian Central Bureau of Statistics provides Mid-year Population. Population prior to 1997 is sourced from the International Monetary Fund. Population is in annual frequency, ending in June of each year

In 1995, Israel had a Jewish population of approximately 4.5 million people, of whom approximately 1.75 million were born abroad. Over one million of these immigrants were born in Europe, with over 650,000 of these born in the former Soviet Union. Despite Poland having the largest Jewish population in the world in the pre-WWII years, the number of Polish Jewish migrants and descendents in Israel was relatively small in 1995 when compared to the USSR due to the impact of the Holocaust.

Outside of Europe, Morocco had the largest number of Jewish immigrants and descendents in Israel by 1995. Morocco had the largest Jewish population in the Muslim world when Israel was founded in 1948, with over 250,000 people. Many Moroccan Jews sought to emigrate to Israel at this time, but often faced resistance from authorities and local populations who believed the Jews would join in the fight against the Arab forces seeking to establish a Muslim state in Palestine. The government of Morocco then officially prohibited emigration to Israel after gaining independence from France in 1956, however this policy was reversed in 1961 whereby the Moroccan government began facilitating Jewish emigration to Israel in return for payments from Jewish organizations in the U.S. and Israel. By the 1970s, Morocco's Jewish population had fallen to less than 15 percent of its size in 1948.

Abstract

The 2017 Census covered all members present in Palestine on the morning of 1st December 2017 regardless of their citizenship, nationality or reasons of presence in Palestine. It also covered the Palestinians temporarily outside of State of Palestine for a period of less than a year for purposes of visit, tourism, treatment or any other reason and who have families inside Palestine. It also covered the detainees and prisoners in Israeli jails regardless of the period of their detention. It did not cover the Palestinians with ID cards and families in Palestine who have been absent for a year or more, with the exception of students studying abroad.

It mainly aims to provide a comprehensive and modern statistical database that creates a digital profile of the Palestinian society in all areas. It additionally fulfills the national needs and in accordance to international recommendations and UN recommendations on the contents of the Census. Consequently, the Census serves as the backbone of planning and developmental policies and strategies formulation.

Geographic coverage

All the administrative levels in West Bank and Gaza Strip

Analysis unit

individuals, households

Universe

All the invidual and Households living in Palestine

Kind of data

Census/enumeration data [cen]

Sampling procedure

1. Sample Frame: It consists of all the enumerated households in the Census 2017 and who are staying in the Palestine at the time of enumeration.

2. Sample Design: A random random sample was selected from each enumeration area enumerated in the census, where the sampling rate reached 20% of the total number of individuals.

Mode of data collection

Personal Interviwe using Tablests

Response rate

Do not apply

Sampling error estimates

There are only Non-sampling errors that occur at any stage during the implementation of census. Many of data quality control procedures were used during the census planning and implementayion to ensure maximum accuracy

Abstract

The main objective of the PHC-2007 is to provide figures for the Palestinian population and their geographical distribution in accordance with a number of relatively stable basic characteristics to inform socioeconomic development purposes. Preparations for conducting censuses take usually 3-5 years for piloting, testing tools, work plans, human and physical needs and timetable. The census is one of the most important statistical activities as it provides statistical data on the distribution of population, and their demographic, social and economic characteristics in a certain reference period of time for all the individuals within the borders of the state.

Geographic coverage

West Bank and Gaza Strip

Analysis unit

individual, household

Universe

The PHC-2007 covered all individuals who were in the Palestinian Territory on the census reference night (30/11-1/12/2007) regardless of nationality and citizenship. It also covered all Palestinians who have usual residence in the Palestinian Territory (including those who were temporarily absent for less than one year for the purpose of visit, tourism, treatment, etc. while their households are still living at their permanent places of residence in the Palestinian Territory). All Palestinian students abroad while their households are still living at their permanent places of residence were also included, in addition to all prisoners and detainees in the Israeli jails regardless of the duration of detention. The census excluded all Palestinians holding identity cards who were absent for more than one year (except for the students), even if their households are still living in their places of residence in the Palestinian Territory

Kind of data

Census/enumeration data [cen]

Sampling procedure

It consists of all the enumerated households in the Census 2007 and who are staying in the west bank at the time of enumeration. We select a systematic random sample from each enumeration area in the Census, and we select a 20% of the total households concluding all the individuals in the household

Mode of data collection

Face-to-face [f2f]

Research instrument

The census questionnaire for the buildings is divided into four parts: Part one: includes identification data, such as: governorate, locality name, locality code, booklet no. in locality, total booklets in locality, no. of completed pages in booklet, enumeration area.
Part two: includes data for all buildings, such as: 1. Building Serial No. in the page 2. Building No. in Enumeration Area 3. Name of the Owner of the Building or Building Name and Address 4. Building Municipality No. 5. Type of Building 6. Type of Ownership 7. Material of External Walls 8. No. of Stories 9. Current Use of Building 10. Establishments Year 11. Total No. Houses in Building Part three: includes data for all Houses, such as: 1. No. of Houses in the Building 2. Current Use of Houses 3. The reason for Closed, Vacant and Deserted Houses Part four: includes data for all Houses used for habitation or work and habitation, such as: 1. Name of the Head of Household No. of Household Members

Cleaning operations

The data processing stage contain of the following operations: 1. Editing before Data Entry At this stage all booklets were edited in the office using the instructions previously prepared for checking to ensure consistent data. 2. Data Entry Program The data entry program was prepared and designed according to the census questionnaire. The program was prepared by using the Oracle database. 3. Data Entry After the completion of the design and the testing of the data entry program and training of data keyers, work began on data entry.

Response rate

100%

Sampling error estimates

Not Applicaple

Trade is a crucial instrument for combating poverty and advancing the Millennium Development Goals, particularly by improving access to international markets for developing countries and fostering a transparent, rules-based, and predictable trading system. To support these goals, the World Bank, in collaboration with international development partners, initiated the Transparency in Trade (TiT) Initiative. This initiative aims to provide free and easily accessible data on trade policies specific to individual countries, allowing policymakers, researchers, and businesses to make informed decisions. Such transparency in trade policy is vital for creating equitable market conditions and ensuring that developing nations can participate in global trade more effectively, thereby contributing to their economic growth and poverty alleviation.

Israel, like many countries, is an active participant in this initiative, which provides a comprehensive understanding of its trade dynamics. The availability of trade-related data from the World Bank’s portal ensures that the nation’s trading policies are part of the broader international effort to make trade more accessible, especially for countries striving to improve their economic standing through global trade.

In contrast to the global trade discussions, the situation in the State of Palestine highlights a different form of crisis—one that pertains to internal displacements caused by conflict and disaster. Conflict and disaster-induced population movements, or "flows," for the State of Palestine are monitored closely due to the region's instability and the ongoing conflict. The most recent data available covers a 180-day period and provides insights into the scale and frequency of these displacements.

Internally displaced persons (IDPs) are defined based on the 1998 Guiding Principles, which describe them as individuals or groups forced to flee or leave their homes due to various causes, including armed conflict, generalized violence, violations of human rights, or natural and human-made disasters. These people remain within their country’s borders, distinguishing them from refugees who cross international boundaries.

The Internal Displacement Monitoring Centre (IDMC) offers event-based data through its Internal Displacement Updates (IDU), which provide initial assessments of internal displacements occurring within the last 180 days. This data is provisional and subject to continuous updates as new information becomes available. The IDU dataset reflects displacement trends from conflicts or disasters and aggregates preliminary estimates from various sources. As more accurate data is compiled and validated, it is made available through the Global Internal Displacement Database (GIDD), which offers a carefully curated and finalized understanding of displacement patterns. This continuous monitoring is essential for understanding the immediate needs of displaced populations and for forming long-term strategies to address internal displacement, particularly in conflict-affected regions like Palestine, where displacements are frequent and complex.

The estimated population of the Gaza Strip for 2023 was around 2.1 million people. The Palestinian population of Gaza is relatively young when compared globally. More than half of Gazans are 19 years or younger. This is due to the comparably high fertility rate in the Gaza Strip of 3.5 children per woman as of 2022.

Israel IL: Refugee Population: by Country or Territory of Origin data was reported at 459.000 Person in 2017. This records a decrease from the previous number of 495.000 Person for 2016. Israel IL: Refugee Population: by Country or Territory of Origin data is updated yearly, averaging 818.500 Person from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 1,541.000 Person in 2007 and a record low of 16.000 Person in 1990. Israel IL: Refugee Population: by Country or Territory of Origin data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Israel – Table IL.World Bank: Population and Urbanization Statistics. Refugees are people who are recognized as refugees under the 1951 Convention Relating to the Status of Refugees or its 1967 Protocol, the 1969 Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa, people recognized as refugees in accordance with the UNHCR statute, people granted refugee-like humanitarian status, and people provided temporary protection. Asylum seekers--people who have applied for asylum or refugee status and who have not yet received a decision or who are registered as asylum seekers--are excluded. Palestinian refugees are people (and their descendants) whose residence was Palestine between June 1946 and May 1948 and who lost their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict. Country of origin generally refers to the nationality or country of citizenship of a claimant.; ; United Nations High Commissioner for Refugees (UNHCR), Statistics Database, Statistical Yearbook and data files, complemented by statistics on Palestinian refugees under the mandate of the UNRWA as published on its website. Data from UNHCR are available online at: www.unhcr.org/en-us/figures-at-a-glance.html.; Sum;

As of January 14, 2025, the UN estimated that around 1.9 million refugees are currently sheltered in school buildings operated by the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA). Around ** percent of Gaza's total population has been displaced due to the ongoing conflict. This was caused by attacks of radical Islamic Hamas' attacks on Israeli territory and the consequent counterstrikes of the Israeli military. Hamas attacks on Israel on October 7, 2023 The radical Islamic Palestinian terror organization Hamas attacked Israel in the early hours of October 7, 2023, on Sabbath - the Jewish day of rest. This strategic attack from Gaza was directed towards Israeli civilians and armed forces. An estimated ***** rockets were fired by Hamas from Gaza toward Israeli targets, hitting civilian buildings and military facilities. Hamas fighters entered the Israeli territory, killing around ***** of Israeli citizens. In the weeks that followed, the Israeli military conducted an aerial bombardment and launched a ground offensive in Gaza. Humanitarian pauses allowed aid to get into Gaza, but fighting has continued since a weeklong cease-fire ended on December 1, 2023. The attacks resulted in a massive escalation of the Middle Eastern conflict, which pushed a political solution further away. The number of civilian casualties in Gaza and Israel is rising each day the conflict continues. Humanitarian situation in Gaza Following Hamas' attacks on Israel, the Israeli military responded with airstrikes against positions of Hamas in the Gaza Strip and cut the region's supply. The Israeli military warned parts of Gaza about their air strikes and urged the population to leave their houses and seek refuge in the centers. Israel isolated the Gaza Strip from the outside world and stopped the supply of electricity, food, and fuel. On October 13, 2023, Israel prompted the Palestinian population of Gaza to vacate the region of northern Gaza. Around one million of the **** million Gazans dwelled in northern Gaza in high density. About half of the Gazan population is underaged. Since the terrorist organization Hamas launched its attacks on Israeli soil on October 7, 2023, around ***** Israelis have died, and ***** have been injured. Through retaliation attacks by the Israeli armed forces against Hamas in Gaza, over 47 thousand Palestinians have died, and around 99 thousand have been injured. There were ***** confirmed Palestinians killed in the Westbank through the ongoing conflict. Hamas's attack on Israel was the deadliest so far.

Perspective-taking is essential for improving intergroup relations. However, it is difficult to implement, especially in violent conflicts. Given that immersive virtual reality (VR) can simulate various points of view (POV), we examined whether it can lead to beneficial outcomes by promoting outgroup perspective-taking, even in armed conflicts. In two studies, Jewish-Israelis watched a 360° VR scene depicting an Israeli-Palestinian confrontation from different POVs–outgroup’s, ingroup’s while imagining outgroup perspective or ingroup’s without imagined perspective-taking. Participants immersed in the outgroup’s POV, but not those who imagined the outgroup’s perspective, perceived the Palestinians more positively than those immersed in the ingroup’s POV. Moreover, participants in the outgroup’s POV perceived the Palestinian population in general more favorably and judged a real-life ingroup transgression more strictly than those in the ingroup’s POV, even five months after VR intervention. Results suggest that VR can promote conflict resolution by enabling effective perspective-taking.

Context

Since 2001, Hamas militants have launched thousands of rocket and mortar attacks on Israel from the Gaza Strip as part of the continuing Arab–Israeli conflict. From 2004 to 2014, these attacks have killed 27 Israeli civilians, 5 foreign nationals, 5 IDF soldiers, and at least 11 Palestinians and injured more than 1900 people, but their main effect is their creation of widespread psychological trauma and disruption of daily life among the Israeli population.

source:

Wikipedia[https://en.wikipedia.org/wiki/Palestinian_rocket_attacks_on_Israel]

Content

This dataset contains the latest rocket alerts released by the Israeli "Home Front Security". The data was aggregated in the http://tzeva-adom.com site.

The column contains date in dd/mm/yy format, time mm:hh format and the name of the area in Hebrew (And sometimes messages like:

הופעלה בישוב בעוטף עזה- ככה''נ איכון שווא which means that it's a false alarm. Those messages can be easily distinguished by the length of them.

Area sizes may differ and does not report exact coordinates.

A list of all the possible areas and messages is in a separate file.

Data is reported from 2013-2014.

Acknowledgements

Context was taken from the Wikipedia page: Palestinian rocket attacks on Israel. Data generated by the Israeli Home Front Command and was made easily accessible to developers(Many apps were created based on it. Reporting the alarm during the last conflict).

The data was aggregated at the site http://tzeva-adom.com.

Inspiration

Israel has a system called "Iron Dome" which intercepts rockets(But only from certain distance). The challenge for Israel is where those systems should be deployed and at what times. Furthermore, it will be interesting to find patterns in the times rockets were being launched, trying to see if different places were targeted in different times of day. Also, areas that were targeted at the same time find if it's possible to cluster the places into different groups of areas.

Operation "Protective Edge" took place from 8 July until 26 August 2014. After it ended, The rocket attack ended (more or less) until today(June 2017). It will be interesting to check out how the operation effects the alerts, the launching patterns, targeted areas, etc.

Though the names in this dataset are in Hebrew, no Hebrew knowledge is needed for working with this dataset. I tried to find an appropriate automatic transliteration service to English, but non of them proved useful. If anyone knows how to get them in English, even using some list from the internet of the cities names in English and their corresponding Hebrew names, I'll appreciate your contribution to the dataset's GitHub repository:

https://github.com/tomersa/tzeva_adom_dataset.git

Also, you may contact me and I'll add the changes.

The world's Jewish population has had a complex and tumultuous history over the past millennia, regularly dealing with persecution, pogroms, and even genocide. The legacy of expulsion and persecution of Jews, including bans on land ownership, meant that Jewish communities disproportionately lived in urban areas, working as artisans or traders, and often lived in their own settlements separate to the rest of the urban population. This separation contributed to the impression that events such as pandemics, famines, or economic shocks did not affect Jews as much as other populations, and such factors came to form the basis of the mistrust and stereotypes of wealth (characterized as greed) that have made up anti-Semitic rhetoric for centuries. Development since the Middle Ages The concentration of Jewish populations across the world has shifted across different centuries. In the Middle Ages, the largest Jewish populations were found in Palestine and the wider Levant region, with other sizeable populations in present-day France, Italy, and Spain. Later, however, the Jewish disapora became increasingly concentrated in Eastern Europe after waves of pogroms in the west saw Jewish communities move eastward. Poland in particular was often considered a refuge for Jews from the late-Middle Ages until the 18th century, when it was then partitioned between Austria, Prussia, and Russia, and persecution increased. Push factors such as major pogroms in the Russian Empire in the 19th century and growing oppression in the west during the interwar period then saw many Jews migrate to the United States in search of opportunity.

Overview

This dataset is the repository for the following paper submitted to Data in Brief:

Kempf, M. A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19. Data in Brief (submitted: December 2023).

The Data in Brief article contains the supplement information and is the related data paper to:

Kempf, M. Climate change, the Arab Spring, and COVID-19 - Impacts on landcover transformations in the Levant. Journal of Arid Environments (revision submitted: December 2023).

Description/abstract

The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This dataset uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant.

Folder structure

The main folder after download contains all data, in which the following subfolders are stored are stored as zipped files:

“code” stores the above described 9 code chunks to read, extract, process, analyse, and visualize the data.

“MODIS_merged” contains the 16-days, 250 m resolution NDVI imagery merged from three tiles (h20v05, h21v05, h21v06) and cropped to the study area, n=510, covering January 2001 to December 2022 and including January and February 2023.

“mask” contains a single shapefile, which is the merged product of administrative boundaries, including Jordan, Lebanon, Israel, Syria, and Palestine (“MERGED_LEVANT.shp”).

“yield_productivity” contains .csv files of yield information for all countries listed above.

“population” contains two files with the same name but different format. The .csv file is for processing and plotting in R. The .ods file is for enhanced visualization of population dynamics in the Levant (Socio_cultural_political_development_database_FAO2023.ods).

“GLDAS” stores the raw data of the NASA Global Land Data Assimilation System datasets that can be read, extracted (variable name), and processed using code “8_GLDAS_read_extract_trend” from the respective folder. One folder contains data from 1975-2022 and a second the additional January and February 2023 data.

“built_up” contains the landcover and built-up change data from 1975 to 2022. This folder is subdivided into two subfolder which contain the raw data and the already processed data. “raw_data” contains the unprocessed datasets and “derived_data” stores the cropped built_up datasets at 5 year intervals, e.g., “Levant_built_up_1975.tif”.

Code structure

1_MODIS_NDVI_hdf_file_extraction.R

This is the first code chunk that refers to the extraction of MODIS data from .hdf file format. The following packages must be installed and the raw data must be downloaded using a simple mass downloader, e.g., from google chrome. Packages: terra. Download MODIS data from after registration from: https://lpdaac.usgs.gov/products/mod13q1v061/ or https://search.earthdata.nasa.gov/search (MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, last accessed, 09th of October 2023). The code reads a list of files, extracts the NDVI, and saves each file to a single .tif-file with the indication “NDVI”. Because the study area is quite large, we have to load three different (spatially) time series and merge them later. Note that the time series are temporally consistent.

2_MERGE_MODIS_tiles.R

In this code, we load and merge the three different stacks to produce large and consistent time series of NDVI imagery across the study area. We further use the package gtools to load the files in (1, 2, 3, 4, 5, 6, etc.). Here, we have three stacks from which we merge the first two (stack 1, stack 2) and store them. We then merge this stack with stack 3. We produce single files named NDVI_final_*consecutivenumber*.tif. Before saving the final output of single merged files, create a folder called “merged” and set the working directory to this folder, e.g., setwd("your directory_MODIS/merged").

3_CROP_MODIS_merged_tiles.R

Now we want to crop the derived MODIS tiles to our study area. We are using a mask, which is provided as .shp file in the repository, named "MERGED_LEVANT.shp". We load the merged .tif files and crop the stack with the vector. Saving to individual files, we name them “NDVI_merged_clip_*consecutivenumber*.tif. We now produced single cropped NDVI time series data from MODIS.
The repository provides the already clipped and merged NDVI datasets.

4_TREND_analysis_NDVI.R

Now, we want to perform trend analysis from the derived data. The data we load is tricky as it contains 16-days return period across a year for the period of 22 years. Growing season sums contain MAM (March-May), JJA (June-August), and SON (September-November). December is represented as a single file, which means that the period DJF (December-February) is represented by 5 images instead of 6. For the last DJF period (December 2022), the data from January and February 2023 can be added. The code selects the respective images from the stack, depending on which period is under consideration. From these stacks, individual annually resolved growing season sums are generated and the slope is calculated. We can then extract the p-values of the trend and characterize all values with high confidence level (0.05). Using the ggplot2 package and the melt function from reshape2 package, we can create a plot of the reclassified NDVI trends together with a local smoother (LOESS) of value 0.3.
To increase comparability and understand the amplitude of the trends, z-scores were calculated and plotted, which show the deviation of the values from the mean. This has been done for the NDVI values as well as the GLDAS climate variables as a normalization technique.

5_BUILT_UP_change_raster.R

Let us look at the landcover changes now. We are working with the terra package and get raster data from here: https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 03. March 2023, 100 m resolution, global coverage). Here, one can download the temporal coverage that is aimed for and reclassify it using the code after cropping to the individual study area. Here, I summed up different raster to characterize the built-up change in continuous values between 1975 and 2022.

6_POPULATION_numbers_plot.R

For this plot, one needs to load the .csv-file “Socio_cultural_political_development_database_FAO2023.csv” from the repository. The ggplot script provided produces the desired plot with all countries under consideration.

7_YIELD_plot.R

In this section, we are using the country productivity from the supplement in the repository “yield_productivity” (e.g., "Jordan_yield.csv". Each of the single country yield datasets is plotted in a ggplot and combined using the patchwork package in R.

8_GLDAS_read_extract_trend

The last code provides the basis for the trend analysis of the climate variables used in the paper. The raw data can be accessed https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 9th of October 2023). The raw data comes in .nc file format and various variables can be extracted using the [“^a variable name”] command from the spatraster collection. Each time you run the code, this variable name must be adjusted to meet the requirements for the variables (see this link for abbreviations: https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary, last accessed 09th of October 2023; or the respective code chunk when reading a .nc file with the ncdf4 package in R) or run print(nc) from the code or use names(the spatraster collection).
Choosing one variable, the code uses the MERGED_LEVANT.shp mask from the repository to crop and mask the data to the outline of the study area.
From the processed data, trend analysis are conducted and z-scores were calculated following the code described above. However, annual trends require the frequency of the time series analysis to be set to value = 12. Regarding, e.g., rainfall, which is measured as annual sums and not means, the chunk r.sum=r.sum/12 has to be removed or set to r.sum=r.sum/1 to avoid calculating annual mean values (see other variables). Seasonal subset can be calculated as described in the code. Here, 3-month subsets were chosen for growing seasons, e.g. March-May (MAM), June-July (JJA), September-November (SON), and DJF (December-February, including Jan/Feb of the consecutive year).
From the data, mean values of 48 consecutive years are calculated and trend analysis are performed as describe above. In the same way, p-values are extracted and 95 % confidence level values are marked with dots on the raster plot. This analysis can be performed with a much longer time series, other variables, ad different spatial extent across the globe due to the availability of the GLDAS variables.

Demographic characteristics for Israeli Jews and Palestinian Arabs by case-control status for overall B-cell non-Hodgkin lymphoma.

The number of fatalities amongst the Palestinian population in the West Bank and Gaza between 2008 and September 2023 stood at ***** and the number of injured people was *******. While the majority of fatalities amongst the Palestinians were in the Gaza Strip, more than half of the injured were based in the West Bank. Hamas attacks on Israel on October 7, 2023 The radical Islamic Palestinian terror organization Hamas attacked Israel in the early hours of October 7, 2023, on Sabbath - the Jewish day of rest. This strategic attack from Gaza was directed towards Israeli civilians and armed forces. An estimated ***** rockets were fired by Hamas from Gaza toward Israeli targets, hitting civilian buildings and military facilities. Hamas fighters entered the Israeli territory, killing around ***** of Israeli citizens. In the weeks that followed, the Israeli military conducted an aerial bombardment and launched a ground offensive in Gaza. Humanitarian pauses allowed aid to get into Gaza, but fighting has continued since a weeklong cease-fire ended on December 1, 2023. The attacks resulted in a massive escalation of the Middle Eastern conflict, which pushed a political solution further away. The number of civilian casualties in Gaza and Israel is rising each day the conflict continues. Before October 2023, there had been four wars between Israel and Hamas. The current conflict is already the deadliest, and the death toll is certain to rise.

Abstract

The Jerusalem Household Social Survey 2013 is one of the most important statistical activities that have been conducted by PCBS. It is the most detailed and comprehensive statistical activity that PCBS has conducted in Jerusalem. The main objective of the Jerusalem household social survey, 2013 is to provide basic information about: Demographic and social characteristics for the Palestinian society in Jerusalem governorate including age-sex structure, Illiteracy rate, enrollment and drop-out rates by background characteristics, Labor force status, unemployment rate, occupation, economic activity, employment status, place of work and wage levels, Housing and housing conditions, Living levels and impact of Israeli measures on nutrition behavior during Al-Aqsa intifada, Criminal offence, its victims, and injuries caused.

Geographic coverage

Social survey data covering the province of Jerusalem only, the type locality (urban, rural, refugee camps) and Governorate

Analysis unit

households, Individual

Universe

The target population was all Palestinian households living in Jerusalem Governorate.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for Jerusalem (J1 and J2) was based on the census implemented by PCBS in 2007 and consisting of enumeration areas. These enumeration areas were used as primary sampling units (PSUs) in the first stage of the sample selection.

The estimated sample size is 1,260 households responding in Jerusalem governorate.

Stratified cluster random sample with two-stages: First stage: Selection of a systematic random sample of 42 enumeration areas (24 EAs in J1 and 18 EAs in J2). Second stage: A sample of 30 responsive households from each enumeration area selected in the first stage.

Sample Strata The population was divided by: 1-Region (Jerusalem J1, Jerusalem J2) 2-Locality type (Jerusalem J1: urban, camp; Jerusalem J2: urban, rural, camp).

Mode of data collection

Face-to-face [f2f]

Research instrument

A survey questionnaire the main tool for gathering information, so do not need to check the technical specifications for the phase of field work, as required to achieve the requirements of data processing and analysis, has been designed form the survey after examining the experience of other countries on the subject of social surveys, covering the form as much as possible the most important social indicators as recommended by the United Nations, taking into account the specificity of the Palestinian community in this aspect.

Cleaning operations

Phase included a set of data processing Activities and operations that have been made to the Forms to prepare her for the analysis phase, This phase included the following operations: Before the introduction of audit data: at this stage was Check all the forms using the instructions To check to make sure the field of logical data and re- Incomplete, including a second field. Data Entry: The data entry Central to the central headquarters in Al-Bireh, was organized The data entry process using the Access Program Where the form has been programmed through this program. Was marked by the program that was developed in the Device properties and features the following: The possibility of dealing with an exact copy of the form The computer screen. The ability to conduct all tests and possibilities Possible and logical sequence of data in the form. Maintain a minimum of errors Portal Digital data or errors of field work. Ease of use and deal with the software and data (User-Friendly). The possibility of converting the data to the other formula can be Use and analysis of the statistical systems Analysis such as SPSS.

Response rate

during the field work we visit 1,820 family in Jerusalem Governorate, where the final results of the interviews were as follows: The number of families who were interviewed (1,188) in Jerusalem Governorate, (715) in J1, (473) in J2.

Sampling error estimates

Accuracy of the Data

Statistical Errors Data of this survey can be affected by statistical errors due to use of a sample. Variance was calculated for the most important indicators and demonstrates the ability to disseminate results for Jerusalem governorate. However, dissemination of data by J1 and J2 area indicates values with a high variance

Non-Statistical Errors It is possible for non-statistical errors to occur at all stages of project implementation or during the collection or entry of data. These errors can be summarized as non-response errors, response errors (respondent), corresponding errors (researcher) and data entry errors. To avoid errors and reduce their impact, strenuous efforts were made in the intensive training of researchers on how to conduct interviews, the procedures that must be followed during the interview and aspects that should be avoided. Practical exercises and theory were covered during the training session. Errors gradually decreased with the accumulation of experience by the field work team, which consisted of permanent and non-permanent researchers who conduct work on every PCBS survey.

In general, non-statistical errors were related to the nature of the Social Survey of Jerusalem and can be summarized as follows: · Many households considered the specific details of the survey as interference in their private lives. · Israeli impact on Palestine (curfew and closure). · Some households thought the survey was related to social assistance or to taxes. · Hesitation by households in the Jerusalem area to supply data because they were afraid of Israeli procedures against them if they participated in a Palestinian survey or activity.

Data Processing The data processing stage consisted of the following operations: 1. Editing and coding prior to data entry: All questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.
2. Data entry: At this stage, data were entered into the computer using a data entry template designed in Access. The data entry program was prepared to satisfy a number of requirements such as:
· Duplication of the questionnaires on the computer screen. · Logic and consistency check of data entered. · Possibility for internal editing of question answers. · Maintaining a minimum of digital data entry and field work errors. · User-friendly handling. · Possibility of transferring data into another format to be used and analyzed using other statistical analytic systems such as SPSS.

Data entry began on April 17, 2013 and finished on July 14, 2013. Data cleaning and checking processes were initiated simultaneously with the data entry. Thorough data quality checks and consistency checks were carried out and SPSS for Windows version 10.0 was used to perform the final tabulation of results.

Possibility of Comparison At this stage, comparison can be made for time series periods and other sources. Where the survey results were compared with the data in 2010. The data were compared with the final results of the Population, Housing and Establishments Census of 2007 for Jerusalem and the results were very consistent.

Descriptive statistics of the outcome variables by condition (Study 1).

Shared suffering can bond groups together and motivate fighting and even dying for the group leading to vicious cycles of violence while the suffering of the outgroup is routinely overlooked. But could the power of shared suffering also be harnessed to reduce tensions? Here we present evidence that a speech appealing to suffering shared by both sides in the Israeli-Palestinian conflict delivered by a professional politician in December 2023 can foster identity fusion, trust and openness to friendship towards the outgroup – Palestinians or Israelis – in a sample of Muslim and Jewish Americans (n = 159). Effects partially persisted three days after exposure to the speech. In a follow-up study we found that the same speech also increased positive attitudes towards Israelis and Palestinians in a sample of the general US population (n = 361).

The number of persons age 18 and older who have completed at least primary education divided by the total population age 18 and older. Highest level of education completed is measured using the IPUMS EDATTAIN variable (EDATTAIN greater or equal 2). The EDATTAIN variable does not necessarily reflect any particular country's definition of the various levels of schooling in terms of terminology or the number of years of schooling. EDATTAIN is an attempt to merge -- into a single, roughly comparable variable -- samples that provide degrees, ones that provide actual years of schooling, and those that have some of both. The variable is largely comparable across census samples at the broadest levels of schooling--less than primary, primary, secondary, and tertiary. For more information, see https://international.ipums.org/international-action/variables/EDATTAIN#description_section. This dataset contains all existing disagregations and, and the latest vintage data for the indicator.Each disaggregation is in a separate column. There is a single row per geography.Data download: CSV File Shape File File GeodatabaseDomain: EDUCATION Subdomain: Educational attainmentGeography Level: Geolev2Measure: PercentageUniverse: Persons age 18+Age Universe: 18+Sex Universe: BothMarital Status Universe: AllNotes:Age is only available as grouped intervals for these samples: United Kingdom 2001; Israel 1972, 1995; Italy 2001; Palestine 1997, 2007; Slovenia 2002. In the AGE variable, data are coded to the mid-points of the intervals (rounding down). The interval for 15 to 19 years (16 to 19 for UK 2001) is coded as 17 in the AGE variable. Thus, this indicator describes persons age 20+ for these samples.