Immigration Data Visualization & Analysis

Overview

This project employs a multi-faceted visual analytics approach to investigate immigration trends to Israel during the years 2020-2022. The goal is to identify demographic shifts, settlement patterns, and the correlation between immigration influxes and municipal Quality of Life (QoL) indices, with a specific focus on the geopolitical impact of 2022.

Background

Immigration (Aliyah) is a core demographic engine of Israel. In 2022, following the outbreak of the Russia-Ukraine war, Israel experienced a dramatic surge in immigration. Understanding the characteristics of this wave (who arrived, where they settled, and why) is crucial for urban planning and social policy. This project utilizes the Tamara Munzner Visualization Model as a strategic framework, selecting specific visual idioms (Sankey, Networks, Maps) to answer distinct analytical "Why" questions and transform raw government data into actionable insights.

Dataset

The project integrates data from multiple official government sources, merged into a unified analytical dataset:

• Immigration Data (2020-2022): Ministry of Aliyah and Integration (Dates, Origin Country, Age, Profession).
• Demographics: Population Registry data on "Localities by Age".
• Geographic Data: "Localities" dataset (Coordinates, District, Distance from borders).
• Socioeconomic Metrics: Central Bureau of Statistics (CBS) Quality of Life Indices 2022.

Key features include:

• Demographic: Age, Gender, Profession, Country of Origin.
• Geographic: Settlement Locality, District, Coordinates (X/Y).
• Temporal: Year and Month of Aliyah.
• Indices: Employment rate, Education levels, Digital literacy, Environmental satisfaction.

Methodology

Data Preparation

1. Cleaning: Handled missing values for 15 immigrants without destination data and 426 with undefined professions.
2. Entity Resolution: Mapped 401 immigration destination entries to the official list of 1,254 localities using fuzzy matching and manual correction.
3. Feature Engineering:
   • Extracted "Continent" from "Country of Origin".
   • Discretized Age into bin groups (0-5, 6-18, 19-45, etc.) to match municipal demographic structures.
4. Translation: Automated Hebrew-to-English translation for categorical variables using Python libraries.

Visualization Techniques (Analytical Arsenal)

The core of the analysis relies on diverse visual idioms to uncover patterns invisible to standard aggregation:

• Interactive Geospatial Maps (Tableau/Folium): To isolate the 2022 anomaly and visualize the intensity of global origin flows against local settlement heatmaps.
• Sankey Diagrams: To decipher the multi-stage flow of Country → Age Group → Gender, revealing the specific demographic profile of the war-driven wave.
• Bipartite Network Graphs: To uncover hidden community preferences by mapping the connection strength between specific origin countries and target cities (e.g., US -> Jerusalem).
• Radar Charts: To benchmark multi-variate Quality of Life metrics across different districts to assess regional strengths.

Results

• The 2022 Surge: A massive spike in immigration occurred in 2022, dominated by arrivals from Russia (~60%) and Ukraine (~20%).
• Demographic Profile: The wave was characterized by the "Young Generation" (ages 19-45), distinct from typical elderly immigration waves.
• Urban Preference: Immigrants overwhelmingly chose major central cities (Tel Aviv, Haifa, Netanya) over peripheral development towns.

Key Findings

1. War-Driven Migration: The demographic profile (young families/individuals) strongly correlates with the Russia-Ukraine war and flight from conscription/conflict.
2. The QoL Paradox: There is no direct correlation between a city's objective Quality of Life score and its popularity among immigrants.
   • Example: Jerusalem attracts high immigration despite ranking lowest (18th) in QoL among major cities.
3. District Strengths: The Tel Aviv District leads in Employment and Education metrics, aligning with the preferences of the younger immigrant demographic.

Implementation Details

The project is organized into the following components:

src/immigration_analysis.py

• Data preprocessing pipeline (merging, cleaning, translating).
• Generation of static Python-based visualizations (WordCloud, Network Graphs).

figures/

• Stores all generated visual assets, including interactive HTML maps (Tableau, Folium) and static high-resolution plots (Sankey diagrams, Radar charts, WordClouds).

Immigration_Project_Report (Hebrew).pdf

Provides in-depth details about the storytelling flow, Munzner’s model application, and statistical findings.

Acknowledgments

• Contributors: Avital Finanser & Sivan Raviv.
• This project was completed as part of the Visualization Analysis & Design course.