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📘 Research Scope: UK Asylum & Immigration (2014–2024)
1. 🎯 Objective
- To understand trends, patterns, and outcomes in asylum applications and immigration flows in the UK over the past decade.
- To highlight policy impacts, demographic changes, and regional distributions.
- To create data-driven insights useful for policymakers, NGOs, academics, and advocacy groups.
2. 📂 Data Sources
You’ll need open, reliable datasets. Recommended sources:
- UK Home Office Immigration Statistics (quarterly & annual datasets) → asylum, resettlement, detention, returns, work/study visas.
- UK Office for National Statistics (ONS) → population migration estimates, demographics.
- Eurostat / UNHCR → asylum comparisons across Europe.
- UK Parliament Briefings → summaries of immigration policy changes.
Key datasets to extract:
- Asylum applications by year, nationality, outcome.
- Immigration by visa type (work, family, study, humanitarian).
- Refugee resettlement and relocation figures.
- Appeals and decisions (granted, refused, pending).
- Detention and removal statistics.
- Demographics: age, gender, region of origin.
3. 🛠 Data Preparation
- Clean and reshape datasets (CSV/Excel from Home Office).
- Use Power Query to merge multiple tables (asylum, visas, population).
- Build time dimension tables for trends.
- Add policy event markers (e.g., Brexit 2016, Afghan resettlement 2021, Rwanda policy 2022).
4. 📊 Power BI Design (Dashboards & Reports)
A. Overview Dashboard
- Total immigration vs asylum applications (2014–2024).
- Year-on-year % changes.
- Major peaks & drops highlighted.
B. Asylum Trends
- Applications by nationality (top 10 countries).
- Decision outcomes (granted, refused, withdrawn, pending).
- Processing time averages.
- Appeals success rate.
C. Immigration Types
- Visa grants by category (study, work, family, humanitarian).
- Share of each visa type in total immigration.
- Regional concentration of arrivals (London vs rest of UK).
D. Demographic Insights
- Age and gender breakdown of asylum seekers.
- Family vs single applicants.
- Vulnerable groups (unaccompanied minors, resettled families).
E. Regional & Local Authority View
- Asylum dispersal by local authority.
- Refugee settlement patterns.
- Regional capacity vs arrivals.
F. Policy Impact Dashboard
- Overlay asylum/immigration numbers with key policy events:
- 2016: Brexit referendum.
- 2020: End of free movement.
- 2021: Afghan resettlement scheme.
- 2022: Ukraine schemes + Rwanda plan.
- Show before/after effect of each policy.
5. 💡 Insights to Extract
- Trend analysis: Has asylum increased or decreased overall? Which nationalities dominate over time?
- Policy effects: Did Brexit reduce EU migration? Did Afghan/Ukraine schemes increase humanitarian visas?
- Backlog issues: How many asylum seekers are awaiting decision compared to past years?
- Regional pressure: Which UK cities/regions host the most asylum seekers?
- Demographic shifts: Are younger or older applicants more common now?
- Comparisons: How does UK asylum acceptance rate compare with EU average?
6. 📈 Deliverables
- Interactive Power BI report with filters (year, nationality, visa type).
- Exportable PDF summary dashboards for quick reference.
- A narrative story page in Power BI explaining the “10-year journey” of asylum and immigration in the UK.
👉 Would you like me to also prepare a sample Power BI data model structure (fact & dimension tables design with relationships), so you know how to organize your datasets before loading?
You said:
search online and give me specific website to check
ChatGPT said:
Here are reliable websites and data sources you can use to gather UK asylum & immigration data for your Power BI project:
✅ Key Official Websites & Data Sources
Source | What you’ll find there | Why useful / notes |
GOV.UK — Migration Statistics / Immigration System Statistics | Quarterly and annual immigration, asylum, irregular migration, visas, returns, citizenship data GOV.UK+1 | Primary source of UK government official data. |
UK Office for National Statistics (ONS): International migration / population & migration | Long-term immigration, emigration, net migration, trends by reason, demographic breakdowns Office for National Statistics+2Office for National Statistics+2 | Good for the “migration context” side of your study. |
House of Commons Library — Research Briefings (Asylum / Migration) | Concise analyses, historical data, policy context, and summaries House of Commons Library+2Research Briefings+2 | Helpful to integrate policy events & explanatory narrative. |
Migration Observatory (University of Oxford) | Briefings, data visualisations, comparative analysis for migration and asylum in the UK Migration Observatory+2Migration Observatory+2 | Very useful for deeper insight, commentary, and data interpretation. |
Asylum Information Database / Asylum in Europe | UK country profile statistics on asylum policy, applications, outcomes Asylum Info Database | To compare UK with European context. |
Refugee Council (UK) | Explainers and recent asylum/refugee statistics, trends explained to public Refugee Council | Good for summary stats / fact‐checking and secondary validation. |
If you like, I can pull together direct data tables / CSV links from these sources (for the past 10 years) that you can load into Power BI. Do you want me to fetch those now?
Datasets Collected
Immigration System
1. Asylum_Visas
2. Asylum Claim
3. Asylum seeking Support
4. Asylum seeking support by local
5. Detention
6. Returns
7. Settlement
8. Citizenship
Economic & Demographic
- UK Population Estimates (ONS)
- Regional Population Distribution (ONS)
- Employment by Nationality (ONS EMP06)
- Average Earnings by Occupation (ONS EARN06)
- Skills Shortage Lists (DfE)
- International Student Enrollment (HESA)
- GDP Growth by Region & Sector (ONS)
Local authority breakdown of asylum support
Recommended Naming Convention
Format
Fact or Dim + Business Meaning + Optional Code in brackets
Example
Fact Visa Applications Vis_D01
Entry and Application Systems
Fact Visa Applications by Nationality and Type
Vis_D01
Fact Visa Outcomes by Nationality
Vis_D02
Fact Asylum Claims Raised
Asy_D01
Fact Asylum Claims by Route of Entry
Asy_D01a
Decision and Outcomes
Fact Asylum Initial Decision Outcomes
Asy_D02
Fact Reasons for Leaving Detention
Det_D03
Fact Returns by Nationality and Return Type
Ret_D01
Fact Returns by Destination Country
Ret_D02
Fact Foreign National Offender Returns by Nationality
Ret_D03
Fact Foreign National Offender Returns by Destination
Ret_D04
Support and Enforcement
Fact Asylum Support Recipients
Asy_D09
Fact Asylum Support by Local Authority
Asy_D11
Fact Detention Entries
Det_D01
Fact Detention Population at Quarter End
Det_D02
Long Term Status and Integration
Fact Settlement Grants by Nationality and Category
Se_D01
Fact Settlement Decisions by Applicant Type
Se_D02
Fact Settlement on Removal of Time Limit
Se_D03
Fact Citizenship Applications
Cit_D01
Fact Citizenship Grants
Cit_D02
How This Should Look in Power BI
In the Fields pane, users should see names like:
Fact Asylum Claims Raised
Fact Asylum Initial Decision Outcomes
Fact Detention Entries
Fact Settlement Grants
Fact Citizenship Grants
Not raw codes.
You can store the original code in:
The query description
A metadata table
The table description in Model view
About project
Provide an overview of the project’s goals and context
Action items
Documents
Embed Google Drive
Embed Figma
✅ README Structure for Your Project
Project Title
UK Immigration Impact Analysis Using Power BI
Objective
To analyze the impact of immigration flows (visas, asylum, settlement, citizenship) on the UK economy, labor market, education, and public services using integrated datasets.
Datasets Collected
Immigration System
1. Visas
- Data_Vis_D01: Applications by nationality & visa type
- Data_Vis_D02: Outcomes by nationality, visa type, and outcome
2. Asylum Claim
- Data_Asy_D01: Asylum claims, 2001 Q1 to 2025 Q3 - dataset
- Data_Asy_D01a: People claiming asylum by route of entry to the UK, 2018 Q1 to 2025 Q3 - dataset
- Data_Asy_D02: Outcomes of asylum claims at initial decision
3. Asylum Support
- Data_Asy_D09: Asylum seekers in receipt of Home Office support
4. Asylum Local Authority
- Data_Asy_D11: Local authority breakdown of asylum support
5. Detention
- Data_Det_D01: Entries into detention by nationality, age, sex
- Data_Det_D02: People in detention
- Data_Det_D03: Reasons for leaving detention
6. Returns
- Data_Ret_D01: Returns by nationality, age, sex, type of return
- Data_Ret_D02: Returns from the UK by return destination
- Data_Ret_D03: Returns of foreign national offenders by nationality
- Data_Ret_D04: Returns of foreign national offenders by return destination
7. Settlement
- Data_Se_D01: Grants of settlement by nationality & category
- Data_Se_D02: Grants and refusals by applicant type
- Data_Se_D03: Grants of settlement on removal of time limit by geographical region of nationality, sex and age
8. Citizenship
- Data_Cit_D01: Applications for British citizenship
- Data_Cit_D02: Grants of citizenship by nationality, sex, age
Economic & Demographic
- UK Population Estimates (ONS)
- Regional Population Distribution (ONS)
- Employment by Nationality (ONS EMP06)
- Average Earnings by Occupation (ONS EARN06)
- Skills Shortage Lists (DfE)
- International Student Enrollment (HESA)
- GDP Growth by Region & Sector (ONS)
Scope of Analysis
Split into two major domains:
1. Asylum & Immigration Analysis
- Flows & Outcomes: Applications, decisions, returns, settlement, citizenship.
- Support Costs: Local authority asylum support, Home Office expenditure.
- Enforcement: Detention entries, reasons for leaving detention.
- Integration: Transition from visas → settlement → citizenship.
2. Economic Impact Analysis
- Labor Market: Employment rates by nationality, skills shortage fulfillment.
- Fiscal Impact: Tax contributions vs. welfare costs.
- Education: International student enrollment and tuition revenue.
- Regional Economy: GDP growth vs. immigration concentration.
- Public Services: Housing, NHS, and local authority costs.
Combined Analysis
- Correlate immigration inflows with economic indicators:
- o Example: High student visa inflows → regional GDP growth & university revenue.
- o Example: Asylum support costs vs. local authority budgets.
- Identify net economic contribution by nationality and visa type.
- Forecast long-term impact using settlement and citizenship trends.
✅ Data Model Design
Fact Tables
- Immigration_Fact: Applications, outcomes, returns, settlement, citizenship.
- Asylum_Fact: Claims, decisions, support costs.
- Economic_Fact: GDP, employment, earnings, tax contributions.
- Education_Fact: Student enrollment, tuition revenue.
Dimension Tables
- Dim_Nationality
- Dim_VisaType
- Dim_Region
- Dim_Time (Year, Quarter, Month)
- Dim_AgeSex
- Dim_Sector (for employment and GDP)
Relationships
- Immigration_Fact → Dim_Nationality, Dim_VisaType, Dim_Time
- Asylum_Fact → Dim_Nationality, Dim_Region, Dim_Time
- Economic_Fact → Dim_Region, Dim_Sector, Dim_Time
- Education_Fact → Dim_Region, Dim_Nationality, Dim_Time
KPIs & Insights
- Visa Approval Rate by nationality and type.
- Asylum Decision Time and success rate.
- Employment Contribution by migrant groups.
- GDP Growth Correlation with immigration inflows.
- Cost vs. Contribution: Welfare vs. tax revenue.
- Student Visa Impact on regional economy.
✅ Do you want me to create a visual Power BI schema diagram for this data model, or write the README in a polished Markdown format ready for GitHub? Or both?